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Ning, Yuan, Yao Wen Liu, Yan Bin Zhang, and Hao Yuan. "Extraction of License Plate Region in License Plate Recognition System." Applied Mechanics and Materials 441 (December 2013): 655–59. http://dx.doi.org/10.4028/www.scientific.net/amm.441.655.
Повний текст джерелаАнотація:
Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.
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Mudda, Avinash, P. Sashi Kiran, Ashish Kumar, and Venkata Sreenivas. "Vehicle Allowance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1085–89. http://dx.doi.org/10.22214/ijraset.2023.50169.
Повний текст джерелаАнотація:
Abstract: License plate detection is an image processing technology that uses a license (number) plate for vehicle identification. The objective is to design and implement an efficient vehicle identification system that identifies the vehicle using the vehicle’s license plate. The system can be implemented at the entrance of parking lots, toll booths, or any private premises like colleges, etc. to keep records of ongoing and outgoing vehicles. It can be used to allow access to only permitted vehicles inside the premises. The developed system first captures the image of the vehicle’s front, then detects the license plate, and then reads the license plate. The vehicle license plate is extracted using image processing of the image. Optical character recognition (OCR) is used for character recognition. The system is implemented using OpenCV and its performance is tested on various images. It is observed that the developed system successfully detects and recognizes the vehicle license plate. To recognize License number plates using the Python programming language. We will utilize OpenCV for this project to identify the license number plates and the python py-tesseract for the characters and digits extraction from the plate. We will build a Python program that automatically recognizes the License Number Plate
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Wang, Rui Feng, Xiao Jin Fu, and Wei Xu. "License Plate Recognition System Design." Applied Mechanics and Materials 738-739 (March 2015): 639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.639.
Повний текст джерелаАнотація:
The license plate recognition system is an important part of modern traffic management. application which is very extensive. In this paper, a method to achieve three main modules split from the image pre-processing, license plate location and character. Image pre-processing module of this article is to image gray and step by Roberts operator edge detection. License plate positioning and segmentation using mathematical morphology is used to determine the license plate location method, and then use the license plate color information of color segmentation method to complete the license plate parts division. This article is to research its main part and use MATLAB to do the image processing simulation.
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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.
Повний текст джерелаАнотація:
Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.
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Mahmood, Zahid, Khurram Khan, Uzair Khan, Syed Hasan Adil, Syed Saad Azhar Ali, and Mohsin Shahzad. "Towards Automatic License Plate Detection." Sensors 22, no. 3 (February 7, 2022): 1245. http://dx.doi.org/10.3390/s22031245.
Повний текст джерелаАнотація:
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.
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Abdullah, Siti Norul Huda Sheikh, Muhammad Nuruddin Sudin, Anton Satria Prabuwono, and Teddy Mantoro. "License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm." Journal of Information Technology Research 5, no. 3 (July 2012): 46–70. http://dx.doi.org/10.4018/jitr.2012070103.
Повний текст джерелаАнотація:
For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.
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Yaba, Hawar Hussein, and Hemin Omer Latif. "Plate Number Recognition based on Hybrid Techniques." UHD Journal of Science and Technology 6, no. 2 (September 1, 2022): 39–48. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp39-48.
Повний текст джерелаАнотація:
Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.
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Molina-Moreno, Miguel, Ivan Gonzalez-Diaz, and Fernando Diaz-de-Maria. "Efficient Scale-Adaptive License Plate Detection System." IEEE Transactions on Intelligent Transportation Systems 20, no. 6 (June 2019): 2109–21. http://dx.doi.org/10.1109/tits.2018.2859035.
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Bhogale, Poonam, Archit Save, Vitrag Jain, and Saurabh Parekh. "Vehicle License Plate Detection and Recognition System." International Journal of Computer Applications 137, no. 9 (March 17, 2016): 31–34. http://dx.doi.org/10.5120/ijca2016908924.
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Adytia, Nico Ricky, and Gede Putra Kusuma. "Indonesian License Plate Detection and Identification Using Deep Learning." International Journal of Emerging Technology and Advanced Engineering 11, no. 7 (July 26, 2021): 1–7. http://dx.doi.org/10.46338/ijetae0721_01.
Повний текст джерелаАнотація:
Abstract— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divided into three main processes, namely license plate detection, character segmentation, and character recognition. The detection process uses transfer learning techniques using Faster R-CNN Inception V2. The segmentation process uses traditional computer vision with morphological operations and contours extraction. Then the character recognition process uses the MobileNet V2 transfer learning technique as an architecture for character classification. The recognition accuracy compared between MobileNet V2 and TesseractOCR shows that MobileNet V2 is superior with an accuracy rate of 96%, while Tesseract-OCR has a poor accuracy of 59%. Keywords— Deep Learning, Convolutional Neural Network, License Plate Detection, Character Segmentation, Character Recognition
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Aggarwal, Akarsh, Anuj Rani, and Manoj Kumar. "A robust method to authenticate car license plates using segmentation and ROI based approach." Smart and Sustainable Built Environment 9, no. 4 (October 4, 2019): 737–47. http://dx.doi.org/10.1108/sasbe-07-2019-0083.
Повний текст джерелаАнотація:
Purpose The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users. Design/methodology/approach From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate. Findings The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent. Research limitations/implications The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city. Originality/value This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.
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AĞGÜL, Burak, and Gökhan ERDEMİR. "Development of a Counterfeit Vehicle License Plate Detection System by Using Deep Learning." Balkan Journal of Electrical and Computer Engineering 10, no. 3 (July 30, 2022): 252–57. http://dx.doi.org/10.17694/bajece.1093158.
Повний текст джерелаАнотація:
In this study, a deep learning-based counterfeit plate detection system that compares and detects vehicles with the make, model, color, and license plate is designed. As known that the relevant government institutions are responsible for keeping all detailed information about all motor vehicles in their database. All registration details are stored in the database. It is possible to find unregistered vehicles by comparing database records with detected details. In general, vehicles with counterfeit license plates are used in illegal actions. Therefore, it is of great importance to detect them. Generally, license plate recognition systems successfully detect counterfeit license plates that are randomly generated. Security units typically use such systems at toll roads, bridge crossings, parking lot entrances and exits, sites, customs gates, etc. This kind of system only checks the plate is exists or not in the database. But it is unsuccessful if the vehicle uses existing plate numbers such as stolen ones. In this study, the developed system can detect not only vehicles' plate numbers but also make, model, year, and color information by using deep learning. Thus, the system can also detect randomly generated plates and stolen plates that belong to another vehicle.
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N, Deeksha, Namitha M. R. Maiya, Varsha S, and Deepashree R. "LICENSE PLATE DETECTION METHODS BASED ON OPENCV." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 316–20. http://dx.doi.org/10.26562/irjcs.2022.v0908.31.
Повний текст джерелаАнотація:
With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic management. License plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and visual studio 2012 under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license platere cognition.
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Pan, Shenghu, Jian Liu, and Dekun Chen. "Research on License Plate Detection and Recognition System based on YOLOv7 and LPRNet." Academic Journal of Science and Technology 4, no. 2 (January 4, 2023): 62–68. http://dx.doi.org/10.54097/ajst.v4i2.3971.
Повний текст джерелаАнотація:
With the development and continuous iteration of digital transformation and artificial intelligence technology, the license plate detection and recognition system based on traditional machine vision and the current deep learning-based license plate recognition system for China is unable to meet the needs of rapid and accurate real-time recognition and recognition in complex environments. This paper designs and integrates a set of license plate detection and recognition system based on YOLOv7, STN and LPRNet models, which can recognize Chinese license plates quickly and accurately in real time, and has good robustness in complex environment. Its average accuracy in complex environments reached 96.1%, indicating that the system has a better effect than the traditional license plate detection and recognition system.
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Adedayo, Kayode David, and Ayomide Oluwaseyi Agunloye. "Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition." Computer and Information Science 14, no. 4 (August 24, 2021): 11. http://dx.doi.org/10.5539/cis.v14n4p11.
Повний текст джерелаАнотація:
License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.
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Jain, Geerisha. "Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing." International Journal of Innovative Technology and Exploring Engineering 11, no. 12 (November 30, 2022): 67–75. http://dx.doi.org/10.35940/ijitee.l9342.11111222.
Повний текст джерелаАнотація:
In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.
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Yoo, Hojin, and Kyungkoo Jun. "Deep Homography for License Plate Detection." Information 11, no. 4 (April 17, 2020): 221. http://dx.doi.org/10.3390/info11040221.
Повний текст джерелаАнотація:
The orientation of plate images in license plate recognition is one of the factors that influence its accuracy. In particular, tilted plate images are harder to detect and recognize characters with than aligned ones. To this end, the rectification of plates in a preprocessing step is essential to improve their performance. We propose deep models to estimate four-corner coordinates of tilted plates. Since the predicted corners can then be used to rectify plate images, they can help improve plate recognition in plate recognition. The main contributions of this work are a set of open-structured hybrid networks to predict corner positions and a novel loss function that combines pixel-wise differences with position-wise errors, producing performance improvements. Regarding experiments using proprietary plate images, one of the proposed modes produces a 3.1% improvement over the established warping method.
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Tawfeeq, Furat, and Yasmine Tabra. "Gate Control System for New Iraqi License Plate." Iraqi Journal for Computers and Informatics 41, no. 1 (December 31, 2014): 1–3. http://dx.doi.org/10.25195/ijci.v41i1.89.
Повний текст джерелаАнотація:
This paper presents an approach to license plate localization and recognition. A proposed method is designed to control the opening of door gate based on the recognition of the license plates number in Iraq. In general the system consists of four stages; Image capturing, License plate cropping, character segmentation and character recognition. In the first stage, the vehicle photo is taken fromstandard camera placed on the door gate with a specific distance from the front of vehicle to be processed by our system. Then, the detection method searches for the matching of the license plate in the image with a standard plate. The segmentation stage is performed by is using edge detection. Then character recognition, done by comparing with template standard numbers and letters used in the Iraqi plate. The system was implemented using Matlab (R2012a) and shows accurate performance results reached 93.33%.
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Alaidi, Abdul Hadi M., Saif Ali Abd Alradha Alsaidi, and Omar Hashim Yahya. "Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm." Journal of Education College Wasit University 1, no. 26 (January 12, 2017): 449–60. http://dx.doi.org/10.31185/eduj.vol1.iss26.102.
Повний текст джерелаАнотація:
This paper presents an automated car license plate recognition system applied for Iraqi vehicle plate number that developed and applied to be used in control and law enforcement related applications. In this work, the proposed license plate recognition consists of three basic stages (preprocessing, license plate localization, license plate recognition). The license plate images are pre-processed through convert image to grayscale and apply morphological transformation filter not convert the result to binary image. Then, blurs the binary image using Gaussian filter and find all contour in image using OpenCV library. In the license plate localization KNN (k-Nearest Neighbors) algorithm are used to find all possible characters in the image. The last step is done by Crop the part of image with highest candidate license plate and apply the preprocessing and license plate localization again to find and recognize all part of license plate in the cropped image.
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Ahire, Pritam, Saiprasad Kadam, and Ajay Jagtap. "Image Enhancement and Automated Number Plate Recognition." International Journal of Science and Healthcare Research 8, no. 2 (April 28, 2023): 178–86. http://dx.doi.org/10.52403/ijshr.20230221.
Повний текст джерелаАнотація:
Automatic Number Plate Recognition (ANPR) and Vehicle Number plate detection (VNPD) systems. ANPR, is a technology that enables automatic detection, recognition, and identification of vehicle license plates, while VNPD is a subset of ANPR that focuses specifically on detecting and recognizing license plates.Several researchers have explored different approaches to ANPR and VNPD systems, as evidenced by the various papers listed. For instance, presented an e security system for vehicles number tracking at a parking lot, proposed a method of monitoring traffic signals, and violations using ANPR and GSM. In order to proceed, the conventional Grab Cut algorithm must first interactively give a candidate frame. for the target detection job to be done.To automate the identification of the licence plate by the Grab Cut algorithm, we update the candidate frame by incorporating the aspect ratio of the licence plate as the foreground extraction feature. Then, to fully implement picture noise reduction, we combined the Bernsen algorithm with the Wiener filter, which is extensively used in the fields of digital signal processing in order to increase the detection precision of conventional target identification techniques. Overall, the papers listed in the question demonstrate the wide-ranging applications of ANPR and VNPD technologies, from parking lot security to traffic signal control to driver assistance systems. These technologies have the potential to improve safety, efficiency, and security on the road, and researchers continue to explore new approaches to their development and implementation. Keywords: Detection of Number Plate, Convolutional Neural Network-(CNN), Object detection, character identification, Machine Learning-(ML).
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Singh, Sweta. "Automatic Car License Plate Detection System: A Review." IOP Conference Series: Materials Science and Engineering 1116, no. 1 (April 1, 2021): 012198. http://dx.doi.org/10.1088/1757-899x/1116/1/012198.
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Ghazi. "A Vehicle License Plate Detection and Recognition System." Journal of Computer Science 8, no. 3 (October 1, 2012): 310–15. http://dx.doi.org/10.3844/jcssp.2012.310.315.
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Park, Se-Ho, Saet-Byeol Yu, Jeong-Ah Kim, and Hyoseok Yoon. "An All-in-One Vehicle Type and License Plate Recognition System Using YOLOv4." Sensors 22, no. 3 (January 25, 2022): 921. http://dx.doi.org/10.3390/s22030921.
Повний текст джерелаАнотація:
In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things.
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Venkateswarlu, K. "YOLO Based Advanced Smart Traffic Assistance Platform for Number Plate and Helmet Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4204–8. http://dx.doi.org/10.22214/ijraset.2023.54414.
Повний текст джерелаАнотація:
Abstract: Now a days road accidents are one of the major causes that are leading to human death. However the most common reason for motorcycle deaths is because many fail to confirm to the law of wearing helmet. Here is the software using YOLO V8 to recognize the motorbike drivers , who are not obeying helmet law in an automated way. The helmet and license plate detection system using YOLO V8 is a computer vision technology-based system that utilizesthe You Only Look Once (YOLO) objectdetection algorithm to detect helmets and license plates in real-time. The system is designed to improve safety on roads and highways by detecting riders without helmets and vehicles without properlicense plates. The system consists of motorcycledetection , helmet and no helmet detection as well as bike license plate recognition. The system is capable of processing image from a variety of sources, including trafficcameras and drones, and can detect the presence or absence of helmets and license plates in the image frames. It uses a deep learning model trained on a large dataset of annotated images to identify and classify objects. The output of the system includes a bounding box around each detected object and a label indicating whether it is a helmet or a license plate. The system can also be configured to generate alerts or notifications when violations are detected. Overall, this system provides a valuable tool for law.
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Et. al., Ria Ambrocio Sagum, MCS. "Incorporating Deblurring Techniques in Multiple Recognition of License Plates from Video Sequences." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 5447–52. http://dx.doi.org/10.17762/turcomat.v12i3.2194.
Повний текст джерелаАнотація:
Recognition of license plate is the process of wherein photographic video or images of license plates are being captured and then processed using an application that implements series of algorithms that will provide the alpha numeric conversion of the captured data. In this study, the researchers developed a license plate recognition that incorporates image deblurring to accommodate multiple recognition from video sequences. The approach uses Background Subtraction and Connected Component Analysis for the detection of license plates, Image deblurring to enhance the image and reduce the difficulties in recognition, and LBP Cascade Classifier was implemented for recognition of characters. Since multiple detection for license plate produces different difficulties such as motion blur and camera angle view the approach attempts to minimize the effects of these problems while still being applicable to multiple detection. 30 videos with 3 minutes length each of actual traffic situation were gathered and recorded at the footbridge of UP Ayala Technohub, Commowealth Ave. Quezon City, Philippines and 10 of these videos were used as input for the testing and experiment of the system. The accuracy for plate detection were computed using F-measure which yields to 87.32% for both system with image deblurring and none, while the accuracy for character recognition is 62.66% for system with image deblurring and 48.25% for the system without image deblurring. The result shows that there is a significant difference in the accuracy of license plate recognition between the system with image deblurring and without image deblurring
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Vaidya, Prof Rajeshri, Vaishnavi Bisen, Manjusha Bansod, Ganesh Masurkar, Lokesh Telange, and Piyush Shelke. "Moving Vehicle Registration Plate Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 544–47. http://dx.doi.org/10.22214/ijraset.2023.50121.
Повний текст джерелаАнотація:
Abstract: License plate detection and E-challan system is an automated solution for traffic law enforcement that utilizes cameras, image processing algorithms, and software to detect license plates of vehicles and issue electronic challans for traffic rule violations. The system involves the installation of cameras at strategic locations on roads and highways, which capture images of passing vehicles. The E-challan system is an efficient and accurate system that reduces the workload of traffic police and enablesquick identification and penalization of traffic violators. E-Challan System is the online platform aimed at providing a wide rangeof support in managing and monitoring the traffic penalties, helping users regarding the problems they face in paying for their challan. The E-challan System is basically an interaction between Police and drivers easily through an online platform or an app. This project prototype describes how challan becomes easy for users through keeping it online. The online platform aims to reducethe paperwork, manual process and increase the convenience for the users
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Wu, Zhan Wen. "License Plate Location Method Based on LOG Edge Detection Operator." Applied Mechanics and Materials 108 (October 2011): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amm.108.52.
Повний текст джерелаАнотація:
The license plate location method is the key technology of license plate recognition system, new algorithm is proposed based on LOG operator detecting edge of License Plate Location. First, a large number of color plate images are preprocessed to remove the background interference information, and then rough location of license plate based on block method, search area of plate will be greatly reduced and accurate positioning the plate will be realized by LOG operator combined with projection method. Static license plate image positioning by simulation and analysis show that the method has high accuracy in license plate location.
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Tao, Ting, Decun Dong, Shize Huang, Wei Chen, and Lingyu Yang. "Object Detection-Based License Plate Localization and Recognition in Complex Environments." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (September 27, 2020): 212–23. http://dx.doi.org/10.1177/0361198120954202.
Повний текст джерелаАнотація:
Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.
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Jose, John Anthony C., Allysa Kate M. Brillantes, Elmer P. Dadios, Edwin Sybingco, Laurence A. Gan Lim, Alexis M. Fillone, and Robert Kerwin C. Billones. "Recognition of Hybrid Graphic-Text License Plates." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (July 20, 2021): 416–22. http://dx.doi.org/10.20965/jaciii.2021.p0416.
Повний текст джерелаАнотація:
Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.
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Hu, Zhong Hua, and Chen Tang. "Research of License Plate Recognition System Based on Labview." Applied Mechanics and Materials 734 (February 2015): 646–49. http://dx.doi.org/10.4028/www.scientific.net/amm.734.646.
Повний текст джерелаАнотація:
The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.
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Hussain, Bydaa Ali, and Mohammed Sadoon Hathal. "Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method." Journal of Engineering 26, no. 7 (July 1, 2020): 115–26. http://dx.doi.org/10.31026/j.eng.2020.07.08.
Повний текст джерелаАнотація:
In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.
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Yang, Bintao. "Studies Advanced in License Plate Recognition." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 512–18. http://dx.doi.org/10.54254/2755-2721/8/20230257.
Повний текст джерелаАнотація:
License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather conditions that can affect recognition accuracy. Ultimately, it is hoped that these insights will contribute to the development of intelligent transportation systems.
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Dr. Govind Shah. "An Efficient Traffic Control System and License Plate Detection Using Image Processing." International Journal of New Practices in Management and Engineering 6, no. 01 (March 31, 2017): 20–25. http://dx.doi.org/10.17762/ijnpme.v6i01.52.
Повний текст джерелаАнотація:
Automatic license plate recognition is extracted from license plate of the vehicle. It is taken as an image or a continuous image taken in sequence. The extracted information can be with or without a database in many applications like electronic payment systems and freeway and arterial monitoring devices for traffic surveillance. ALPR employs CC camera, advanced camera or black and white, color camera to capture the image. ALPR is fruitful if the captured images are of good quality. ALPR is a real time application that processes the images of license plates in various conditions like dark or bright times in a day. A general technique should be identified to process images in many different countries or states. We should know that the license plate generally consists of various colors, languages, fonts and others have images in the background. Also, these plates are obstructed by mud, light, some accessories especially on a car. Here, we discuss about methods for ALPR. We classify ALPR based on the features they are used in each method and knowing their advantages, disadvantages, recognition accuracy and processing speed. Managing the timing in traffic controlling by calculating the density of an image.
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Selmi, Zied, Mohamed Ben Halima, Umapada Pal, and M. Adel Alimi. "DELP-DAR system for license plate detection and recognition." Pattern Recognition Letters 129 (January 2020): 213–23. http://dx.doi.org/10.1016/j.patrec.2019.11.007.
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Mb Aljelawy, Qudes, and Tariq M. Salman. "License plate recognition in slow motion vehicles." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2236–44. http://dx.doi.org/10.11591/beei.v12i4.4990.
Повний текст джерелаАнотація:
The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.
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Mb Aljelawy, Qudes, and Tariq M. Salman. "License plate recognition in slow motion vehicles." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2236–44. http://dx.doi.org/10.11591/eei.v12i4.4990.
Повний текст джерелаАнотація:
The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.
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Wang, Ye Qin, Liang Hai Chen, and Li Yun Zhuang. "Research on License Plate Recognition System Based on Computer Vision." Applied Mechanics and Materials 65 (June 2011): 536–41. http://dx.doi.org/10.4028/www.scientific.net/amm.65.536.
Повний текст джерелаАнотація:
In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for program transfer and results display. The experimental results showed that the location of license plate was exact and the character recognition rate was high.
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Kundrotas, Mantas, Jūratė Janutėnaitė-Bogdanienė, and Dmitrij Šešok. "Two-Step Algorithm for License Plate Identification Using Deep Neural Networks." Applied Sciences 13, no. 8 (April 13, 2023): 4902. http://dx.doi.org/10.3390/app13084902.
Повний текст джерелаАнотація:
License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average.
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Lin, Cheng-Jian, Chen-Chia Chuang, and Hsueh-Yi Lin. "Edge-AI-Based Real-Time Automated License Plate Recognition System." Applied Sciences 12, no. 3 (January 28, 2022): 1445. http://dx.doi.org/10.3390/app12031445.
Повний текст джерелаАнотація:
The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost.
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Poad, Farhana Ahmad, Noor Shuraya Othman, Roshayati Yahya Atan, Jusrorizal Fadly Jusoh, and Mumtaz Anwar Hussin. "Automated detection of vehicles license plate using image processing techniques." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1408. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1408-1415.
Повний текст джерелаАнотація:
The aim of this project is to design an Automated Detection of License Plate (ADLP) system based on image processing techniques. There are two techniques that are commonly used in detecting the target, which are the Optical Character Recognition (OCR) and the split and merge segmentation. Basically, the OCR technique performs the operation using individual character of the license plate with alphanumeri characteristic. While, the split and merge segmentation technique split the image of captured plate into a region of interest. These two techniques are utilized and implemented using MATLAB software and the performance of detection is tested on the image and a comparison is done between both techniques. The results show that both techniques can perform well for license plate with some error.
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Kale, Tushar. "Helmet Detection and Number Plate Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4036–40. http://dx.doi.org/10.22214/ijraset.2023.52559.
Повний текст джерелаАнотація:
Abstract: Developing countries have always relied on motorcycles as their primary mode of transportation, but unfortunately, the number of motorcycle accidents has been on the rise in recent years. One of the leading causes of fatalities in these accidents is the lack of helmet usage by motorcyclists. To ensure that motorcyclists wear helmets, traditional methods include manual monitoring by traffic police at intersections or the use of CCTV footage to identify those not wearing a helmet. However, these methods require significant human effort and intervention. This system proposes an automated approach to identify nonhelmeted motorcyclists and retrieve their license plate information from CCTV footage. The system first differentiates moving objects as motorcycles or non-motorcycles. For classified motorcyclists, the system identifies whether they are wearing helmets or not. If the motorcyclist is not wearing a helmet, the system extracts the license plate number using an OCR algorithm.
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Liyana Yaacob, Nur, Ammar Ahmed Alkahtani, Fuad M. Noman, Ahmad Wafi Mahmood Zuhdi, and Dhuha Habeeb. "License plate recognition for campus auto-gate system." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (January 1, 2021): 128. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp128-136.
Повний текст джерелаАнотація:
<p><span>Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.</span></p>
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Deshpande, Meena, M. B. Veena, and Alachew Wubie Ferede. "Auditory Speech Based Alerting System for Detecting Dummy Number Plate via Video Processing Data sets." Computational Intelligence and Neuroscience 2022 (September 2, 2022): 1–12. http://dx.doi.org/10.1155/2022/4423744.
Повний текст джерелаАнотація:
Spectrum of applications in computer vision use object detection algorithms driven by the power of AI and ML algorithms. State of art detection models like faster Region based convolutional Neural Network (RCNN), Single Shot Multibox Detector (SSD), and You Only Look Once (YOLO) demonstrated a good performance for object detection, but many failed in detecting small objects. In view of this an improved network structure of YOLOv4 is proposed in this paper. This work presents an algorithm for small object detection trained using real-time high-resolution data for porting it on embedded platforms. License plate recognition, which is a small object in a car image, is considered for detection and an auditory speech signal is generated for detecting fake license plates. The proposed network is improved in the following aspects: Training the classifier by using positive data set formed from the core patterns of an image. Training YOLOv4 by the features obtained by decomposing the image into low frequency and high frequency. The resultant values are processed and demonstrated via a speech alerting signals and messages. This contributes to reducing the computation load and increasing the accuracy. Algorithm was tested on eight real-time video data sets. The results show that our proposed method greatly reduces computing effort while maintaining comparable accuracy. It takes 45 fps to detect one image when the input size is 1280 × 960, which could keep a real-time speed. Proposed algorithm works well in case of tilted, blurred, and occluded license plates. Also, an auditory traffic monitoring system can reduce criminal attacks by detecting suspicious license plates. The proposed algorithm is highly applicable for autonomous driving applications.
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Zhai, Weifang, Terry Gao, and Juan Feng. "Research on Pre-Processing Methods for License Plate Recognition." International Journal of Computer Vision and Image Processing 11, no. 1 (January 2021): 47–79. http://dx.doi.org/10.4018/ijcvip.2021010104.
Повний текст джерелаАнотація:
The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.
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Raza, Muhammad Ali, Chun Qi, Muhammad Rizwan Asif, and Muhammad Armoghan Khan. "An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model." Applied Sciences 10, no. 6 (March 22, 2020): 2165. http://dx.doi.org/10.3390/app10062165.
Повний текст джерелаАнотація:
License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.
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C, Adarsha. "Automated Vehicle Noise and Over Speed Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4796–800. http://dx.doi.org/10.22214/ijraset.2022.45097.
Повний текст джерелаАнотація:
Abstract: This paper introduces anongoing project on the surveillance of speed vehicles and which makes more noise on the road. Noise pollution created by vehicles on urban roads is becoming more severe. To enforce current measures,we developed a vehicular noise surveillance system including a vehicle speed measurement method. Samples ofvehicular noise were recorded on-site using IR sensor. When IR Sensor detects more vehicle noise greater than 90 decibels, then the transmitter sends the data to the receiver. The receiver recieves the data then makes the RaspberryPi camera on. RaspberryPi camera captures the vehicle number plate and rider photo or video using OCR and the buzzer will turn on it gives the intimation and at the same time the data will store in cloud. License Platform Detection is a computer technology that enables us to identify digital images on the platform automatically. Different operations are covered in this system,such as imaging, number pad locations, alphanumeric character truncation and OCR. The final objective of the system is to construct and create efficient image processing procedures and techniques to position a licensing platter on the Open Computer View Library picture. It was used and implemented the KNN algorithm and python programming language. The technology can be used in different industries such as security, highway speed detection, lighting violations, manuscript documents, automatic charging system, etc. Auto plate recognition is an integratedtechnology which identifies the auto licence plate. Auto plate auto recognition. Multiple applications include complex safety systems, public spaces, parking andurban traffic control. Automatic Vehicle License Plate Recognition (AVLPR) has undesirable aspects because of many effects, such as light and speed. This work presents an alternative technique to leverage free software for the implementation of AVLPR systems including Python and the Open ComputerVision (openCV).
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Moustafa, Akram A., and Mohammed-Issa Riad Mousa Jaradat. "A New Approach for License Plate Detection and Localization: Between Reality and Applicability." International Business Research 8, no. 11 (October 26, 2015): 13. http://dx.doi.org/10.5539/ibr.v8n11p13.
Повний текст джерелаАнотація:
<p>License Plate Detection and Localization (LPDL) is known to have become one of the most progressive and growing areas of study in the field of Intelligent Traffic Management System (ITMS). LPDL provides assistance by being able to specifically locate a vehicle’s number plate which is an essential part of ITMS, that is used for automatic road tax collection, traffic signals defilement implementation, borders and payments barriers and to monitor unlike activities. Organizations can deploy the number plate detection and recognition system to track their vehicles and to monitor each of them in their vital business activities like inbound and outbound logistics, find the exact location of their vehicles and organize entrance management. A competent algorithm is proposed in this paper for number plate detection and localization based on segmentation and morphological operators. Thus, the proposed algorithm it works on enhancing the quality of the image by applying morphological operators afterwards to segment out license plate from the captured image. No assumptions about the license plate color, style of font, size of text and type of material the plate is made of. The results reveal that the proposed algorithm works perfectly on all kinds of license plates with 93.43% efficiency rate. </p>
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Abbass, Ghida, and Ali Marhoon. "Iraqi License Plate Detection and Segmentation based on Deep Learning." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (August 25, 2021): 102–7. http://dx.doi.org/10.37917/ijeee.17.2.12.
Повний текст джерелаАнотація:
Nowadays, the trend has become to utilize Artificial Intelligence techniques to replace the human's mind in problem solving. Vehicle License Plate Recognition (VLPR) is one of these problems in which the computer outperforms the human being in terms of processing speed and accuracy of results. The emergence of deep learning techniques enhances and simplifies this task. This work emphasis on detecting the Iraqi License Plates based on SSD Deep Learning Algorithm. Then Segmenting the plate using horizontal and vertical shredding. Finally, the K-Nearest Neighbors (KNN) algorithm utilized to specify the type of car. The proposed system evaluated by using a group of 500 different Iraqi Vehicles. The successful results show that 98% regarding the plate detection, and 96% for segmenting operation.
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Shi, Gui Ming, Tong Wu, Hang Su, and Qing Tao Wei. "Research on Identification Technology of Vehicle License Plate Based on Image Processing." Applied Mechanics and Materials 513-517 (February 2014): 2827–30. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2827.
Повний текст джерелаАнотація:
Automatic vehicle license plate is an important part of intelligent transportation system. The success of the plate recognition will have a deep impact on the construction of intelligent transport systems. Image processing, tilt correction, character delimitation, character recognition and matching are main applications of vehicle license plate recognition, and the above process are implemented in matlab environment. Vehicle license plate location is implemented by vehicle license plate locating method based on edge detection and morphology filter in this article. The tilt correction mode based on Hough transform is used for license plate tilt correction section; character delimitation algorithm is used for character delimitation to achieve the vehicle license plate character segmentation; character recognition method based on template matching is chosen for character recognition section in this article, and successfully identify the license plate number.
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Tu, Chunling, and Shengzhi Du. "A hierarchical RCNN for vehicle and vehicle license plate detection and recognition." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 731. http://dx.doi.org/10.11591/ijece.v12i1.pp731-737.
Повний текст джерелаАнотація:
<span>Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.</span>