Relevant bibliographies by topics / YOLO ALGORITHMS

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'YOLO ALGORITHMS'

Author: Grafiati
Published: 16 January 2024

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Journal articles on the topic "YOLO ALGORITHMS"

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Wan, Chengjuan, Yuxuan Pang, and Shanzhen Lan. "Overview of YOLO Object Detection Algorithm." International Journal of Computing and Information Technology 2, no. 1 (August 25, 2022): 11. http://dx.doi.org/10.56028/ijcit.1.2.11.

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As an important research direction in the field of computer vision, object detection has developed rapidly and many kinds of mature algorithms emerged. The series of YOLO (You Only Look Once) algorithms implement one-stage detection based on regression ideas, which showing preeminent in speed and owning strong generalization on a variety of datasets. This paper will give a simple introduction to the current mainstream deep learning object detection algorithm, then focus on combing the principle and optimizational process of the series of YOLO algorithms, summarize the latest breakthroughs in YOLO algorithm, Hopefully that can provide reference for the research of related topics.
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Kadhum, Aseil Nadhum, and Aseel Nadhum Kadhum. "Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic." June-July 2023, no. 34 (July 29, 2023): 27–35. http://dx.doi.org/10.55529/jipirs.34.27.35.

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Artificial Intelligence and robotics the fields in which there is necessary required object detection algorithms. In this study, YOLO and different versions of YOLO are studied to find out advantages of each model as well as limitations of each model. Even in this study, YOLO version similarities and differences are studied. Improvement in the YOLO (You Only Look Once) as well as CNN (Convolutional Neural Network) is the research study present going on for different object detection. In this paper, each YOLO version model is discussed in detail with advantages, limitations and performance. YOLO updated versions such as YOLO v1, YOLO v2, YOLO v3, YOLO v4, YOLO v5 and YOLO v7 are studied and showed superior performance of YOLO v7 over other versions of YOLO algorithm.
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Zhou, Xuan, Jianping Yi, Guokun Xie, Yajuan Jia, Genqi Xu, and Min Sun. "Human Detection Algorithm Based on Improved YOLO v4." Information Technology and Control 51, no. 3 (September 23, 2022): 485–98. http://dx.doi.org/10.5755/j01.itc.51.3.30540.

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The human behavior datasets have the characteristics of complex background, diverse poses, partial occlusion, and diverse sizes. Firstly, this paper adopts YOLO v3 and YOLO v4 algorithms to detect human objects in videos, and qualitatively analyzes and compares detection performance of two algorithms on UTI, UCF101, HMDB51 and CASIA datasets. Then, this paper proposed an improved YOLO v4 algorithm since the vanilla YOLO v4 has incomplete human detection in specific video frames. Specifically, the improved YOLO v4 introduces the Ghost module in the CBM module to further reduce the number of parameters. Lateral connection is added in the CSP module to improve the feature representation capability of the network. Furthermore, we also substitute MaxPool with SoftPool in the primary SPP module, which not only avoids the feature loss, but also provides a regularization effect for the network, thus improving the generalization ability of the network. Finally, this paper qualitatively compares the detection effects of the improved YOLO v4 and primary YOLO v4 algorithm on specific datasets. The experimental results show that the improved YOLO v4 can solve the problem of complex targets in human detection tasks effectively, and further improve the detection speed.
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Liu, Tao, Bo Pang, Lei Zhang, Wei Yang, and Xiaoqiang Sun. "Sea Surface Object Detection Algorithm Based on YOLO v4 Fused with Reverse Depthwise Separable Convolution (RDSC) for USV." Journal of Marine Science and Engineering 9, no. 7 (July 7, 2021): 753. http://dx.doi.org/10.3390/jmse9070753.

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Unmanned surface vehicles (USVs) have been extensively used in various dangerous maritime tasks. Vision-based sea surface object detection algorithms can improve the environment perception abilities of USVs. In recent years, the object detection algorithms based on neural networks have greatly enhanced the accuracy and speed of object detection. However, the balance between speed and accuracy is a difficulty in the application of object detection algorithms for USVs. Most of the existing object detection algorithms have limited performance when they are applied in the object detection technology for USVs. Therefore, a sea surface object detection algorithm based on You Only Look Once v4 (YOLO v4) was proposed. Reverse Depthwise Separable Convolution (RDSC) was developed and applied to the backbone network and feature fusion network of YOLO v4. The number of weights of the improved YOLO v4 is reduced by more than 40% compared with the original number. A large number of ablation experiments were conducted on the improved YOLO v4 in the sea ship dataset SeaShips and a buoy dataset SeaBuoys. The experimental results showed that the detection speed of the improved YOLO v4 increased by more than 20%, and mAP increased by 1.78% and 0.95%, respectively, in the two datasets. The improved YOLO v4 effectively improved the speed and accuracy in the sea surface object detection task. The improved YOLO v4 algorithm fused with RDSC has a smaller network size and better real-time performance. It can be easily applied in the hardware platforms with weak computing power and has shown great application potential in the sea surface object detection.
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Chen, Xin, Peng Shi, and Yi Hu. "A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C." Journal of Marine Science and Engineering 11, no. 7 (July 24, 2023): 1475. http://dx.doi.org/10.3390/jmse11071475.

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Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object boundaries. YOLO-C surpasses existing methods in terms of accuracy and detection scope. Compared to U-Net, it achieves an impressive 15.17% improvement in mean pixel accuracy (mPA). With a processing speed of 98 frames per second, YOLO-C meets the requirements of real-time detection and provides accurate size estimation through segmentation. Furthermore, it achieves a mean average precision (mAP) of 58.94% and a mean intersection over union (mIoU) of 70.36%, outperforming industry-standard algorithms such as YOLOX. Because of the good performance in both rapid processing and high precision, YOLO-C can be effectively utilized in real-time seabed exploration tasks.
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Cong, Xiaohan, Shixin Li, Fankai Chen, Chen Liu, and Yue Meng. "A Review of YOLO Object Detection Algorithms based on Deep Learning." Frontiers in Computing and Intelligent Systems 4, no. 2 (June 25, 2023): 17–20. http://dx.doi.org/10.54097/fcis.v4i2.9730.

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Object detection is a research hotspot in the field of computer vision, and YOLO series shows good performance in object detection, and has been widely used in robot vision, unmanned driving and other fields in recent years. This paper first introduces the YOLO series algorithm, including the principle, innovation points, advantages and disadvantages of various algorithms, then introduces the application field of YOLO series, and finally analyzes its future development trend to provide reference for the topic research.
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Karmakar, Malay. "Face Recognition Technique using YOLO V5 Algorithm." International Research Journal of Computer Science 10, no. 03 (March 31, 2023): 04–12. http://dx.doi.org/10.26562/irjcs.2023.v1002.01.

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In today’s date real world application like human-machine interaction, security surveillance face recognition has made its great importance. For face recognition the steps to be followed are data collection, preprocessing, Feature Extraction, Training Evaluation and finally testing. One of the best Algorithms used for face recognition is Viola-Jones Algorithm. Viola Jones Algorithm is highly accepted because of its fast processing time and high detection rate. The other detection Algorithms which can be used are HOG Algorithm (Histogram Oriented Gradient), Deep Learning CNN (Convolution Neural Network) Algorithm, Haar cascade Algorithm and MTCNN Algorithm. In this Paper we will discuss about all this Algorithms and compare the detection rate of facial points among various methods for the datasets. YOLO V5 algorithm is another algorithm which can be used for face recognition technique. Face recognition can be achieved by combining the YOLO V5 algorithm with additional technique such as deep face recognition models like face net, which can recognize and compare facial features to identify individuals.YOLOV5 can identify facial features such as eyes, mouth, and nose, making it useful for a variety of face recognition applications.
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Gao, Ruizhen, Shuai Zhang, Haoqian Wang, Jingjun Zhang, Hui Li, and Zhongqi Zhang. "The Aeroplane and Undercarriage Detection Based on Attention Mechanism and Multi-Scale Features Processing." Mobile Information Systems 2022 (September 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/2582288.

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Undercarriage device is one of the essential parts of an aeroplane, and accurate detection of whether the aeroplane undercarriage is operating normally can effectively avoid aeroplane accidents. To address the problems of low automation and low accuracy of small target detection in existing aeroplane undercarriage detection methods, an improved algorithm for aeroplane undercarriage detection YOLO V4 is proposed. Firstly, the convolutional network structure of Inception-ResNet is integrated into the CSPDarkNet53 framework to improve the algorithm’s ability to extract semantic information of target features; then an attention mechanism is added to the path aggregation network algorithm structure to improve the importance and relevance of different features after conceptual operations. In addition, aeroplane and undercarriage datasets were constructed, and finally, the generated partitioned test sets were tested to evaluate the test performance of Faster R-CNN, YOLO V3, and YOLO V4 target detection algorithms. The experimental results show that the improved algorithm has significantly improved the recall rate and the mean accuracy of detection for small targets in our dataset compared with the YOLO V4 algorithm. The reasonableness and advancedness of the improved algorithm in this paper are effectively verified.
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Liu, Jiayi, Xingfei Zhu, Xingyu Zhou, Shanhua Qian, and Jinghu Yu. "Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm." Electronics 11, no. 10 (May 13, 2022): 1561. http://dx.doi.org/10.3390/electronics11101561.

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Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOLO-V5. Finally, the K-means++ clustering algorithm was applied to reduce the sensitivity to the initial clustering center, making the positioning more accurate and reducing the network loss. The proposed YOLO-SO model was compared with other object detection algorithms such as YOLO-V3, YOLO-V4, and Faster R-CNN. Experimental results demonstrated that the YOLO-SO model reaches 84.0% mAP, 5.5% higher than the original YOLO-V5 algorithm. Moreover, the YOLO-SO model had clear advantages in terms of the smallest weight size and detection speed of 25 FPS. These advantages make the YOLO-SO model more suitable for the real-time detection of metal TO-base appearance defects.
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Li, Zhuang, Jianhui Yuan, Guixiang Li, Hao Wang, Xingcan Li, Dan Li, and Xinhua Wang. "RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO." Sensors 23, no. 14 (July 14, 2023): 6414. http://dx.doi.org/10.3390/s23146414.

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With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are used to strengthen the features fused by the neural network. The multi-scale feature fusion structure of the original network based on a PANet structure is improved to a weighted bidirectional feature pyramid structure to achieve more efficient and richer feature fusion. In addition, a small object detection layer is added, and the loss function is modified to optimise the network model. The experimental results from four remote sensing image datasets, such as DOTA and NWPU-VHR 10, indicate that RSI-YOLO outperforms the original YOLO in terms of detection performance. The proposed RSI-YOLO algorithm demonstrated superior detection performance compared to other classical object detection algorithms, thus validating the effectiveness of the improvements introduced into the YOLOv5 algorithm.
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Dissertations / Theses on the topic "YOLO ALGORITHMS"

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Marmayohan, Nivethan, and Abdirahman Farah. "Scene analysis using Tensorflow & YOLO algorithms on Raspberry pi 4." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45540.

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Objektdetektion är en av de viktigaste mjukvarukomponenterna i nästa generation trafikövervakning. Deep learnings-algoritmer för objektdetektion, exempelvis YOLO (You Only Look Once), är snabba och noggranna algoritmer i realtid. Realtidsdetektion och igenkänning av objekt är viktiga uppgifter för bildbehandling.  I denna studie presenteras ett inbäddat system för detektion och igenkänning av objekt i normal videohastighet (realtid). Indata är följaktligen en videoström som härstammar från en trafikmiljö i Halmstad. Hårdvaran  är Raspberry pi 4 i vilken programvarupaketen Tensorflow, YOLO  samt  träningskonceptet ”Transfer learning” har implementerats. Resultaten presenteras i form av kvantifiering av realtidskörning på FPS (frames per second), detektion  noggrannhet, CPU-temperatur och CPU-frekvens i olika experiment. En slutsats är att Raspberry pi 4 kan utföra objektklassificering och detektion med hög noggrannhet i en del scenarier för trafikövervakning med YOLO-algoritmer. Ett scenario för att klassificera objekt med långsam hastighet till exempel gående, skulle det vara genomförbar med att klassificera och detektera med en högnoggrannhet. För objekt med höghastighet som bilar och cyklister så har Raspberry pi 4 svårt att detektera och klassificera objekter.
Object detection is one of the essential software components in the next generation of traffic monitoring. Real-time detection and recognition of objects are essential tasks for image processing. Therefore, deep learning algorithms for object detection such as YOLO (You Only Look Once) are increasingly used in image analysis, since they run in normal video frame rate (real-time)  and are reasonably accurate. This study presents an embedded system and its results for detecting and recognizing objects in real-time. Results are based on a video stream originating from a traffic environment in the city of  Halmstad (Sweden). The embedded system is implemented in Raspberry pi 4 using the software Tensorflow and different deep learning algorithms of the YOLO software package. Real-time analyses on frames per second, accuracy in mean average precision, CPU temperature, and CPU frequency are reported for experiments comprising transfer learning. A main conclusion is that Raspberry pi 4 can perform object classification and detection with high accuracy in certain scenarios for traffic monitoring with YOLO algorithms. For example, classifying objects with the speed of a pedestrian would be feasible with classifying and detecting with high accuracy. On the other hand, with high-speed objects such as cars and cyclists, it is a more challenging task for Raspberry pi 4 to detect and classify objects.
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Donini, Massimo. "Algoritmi di stitching per il rilevamento dell'occupazione di aule in un contesto smart campus." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19063/.

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L'obiettivo di questo progetto è studiare un sistema con dispositivi a basso costo in grado di determinare l'occupazione corrente di un'aula, pensato principalmente per ottimizzare la gestione degli spazi universitari o di grandi edifici, implementandone un prototipo. A supporto di ciò verranno presentati algoritmi di image stitching per fornire una visione ottimale anche degli ambienti più ampi. In questo progetto è stato implementato un algoritmo di rilevamento dell'occupazione utilizzando componenti hardware economici che seguono una logica programmata appositamente. L'approccio impiegato si basa sull'utilizzo di camere in posizione prospettica, affidando ad una rete neurale l'elaborazione delle immagini. La motivazione di questa scelta deriva dal fatto che le tecnologie basate su camere sono attualmente più accurate ed utilizzarle in posizione prospettica si è rivelato più adeguato, sulla base della conformazione delle aule con più ingressi. La rete neurale utilizzata è stata YOLOv3, estremamente veloce ed accurata, mentre l'hardware che è stato scelto dopo averlo confrontato con altri simili è il Raspberry Pi 2 Model B, uno dei single-board computer più venduti al mondo. Questa scelta è stata motivata dalla capacità computazionale richiesta e dall'obiettivo di limitare i costi. Per poter applicare l'algoritmo di occupancy detection anche a stanze più ampie è stato necessario adottare un metodo di image stitching, col quale si è potuto combinare immagini provenienti da più camere per produrre una foto panoramica. In questo caso si è preferito sviluppare un metodo ad hoc invece di utilizzare software già presenti sul mercato in quanto essi necessitano di una capacità computazionale e di risorse elevate.
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ARYA, DEEPRAJ. "POTHOLE DETECTION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20452.

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Roads are the most important form of nation's transportation system. It is extremely crucial to maintain them in good situation. Potholes are a type of road problem that can harm vehicles and have a detrimental impact on drivers' ability to drive safely, which can result in traffic accidents. Potholes that develop on the road must be filled to keep the roadways in excellent condition. It is essential that you keep them in good shape. It can be difficult to locate potholes in the road, particularly in India where there are millions of km of roadways. In a complicated road environment, effective and proactive management of potholes is crucial for ensuring driver safety. Driver safety is significantly improved by effective and proactive treatment of potholes in a complex road environment. Additionally, it is anticipated to help maintain traffic flow and assist to the reduction of traffic accidents. To get around this problem, a number of strategies have been developed, including manual reporting and government initiatives to help auto-detect pothole zone. There have been several strategies created to get around this problem, from human reporting to authorities to take action for automatic detection of pothole zones. Automated methods for spotting potholes have recently been developed, and these systems include a number of fundamental technologies, including sensors and signal processing. Considering the technology used in the process of identifying potholes, three different types of automated pothole detection systems can be categorized- methods based on vision, vibration, and 3D reconstruction. Building an autonomous model of pothole detection is the major goal of this endeavour, which aims to find potholes as soon as possible. Therefore, it is necessary to automate pothole detection with high speed and real-time accuracy. Our major objective is to train and analyze the YOLOv5, YOLOv7, and YOLOv8 model for pothole identification. These models are trained using a collection of data for potholes images, and the results are examined by assessing the model's accuracy, computational speed, recall, and size, which are then contrasted with those of previous YOLO algorithms. The methodology used in this paper will greatly aid in road maintenance by decreasing expenses and speeding up the detection of potholes. In this article, 84.6%, 87.1% and 85.4% accuracy has been achieved for yolov5, yolov7 and yolov8 model respectively.
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Farinha, João Simões. "In-vehicle object detection with YOLO algorithm." Master's thesis, 2018. http://hdl.handle.net/1822/64273.

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Dissertação de mestrado em Computer Science Engineering
With the growing computational power that we have at our disposal and the ever-increasing amount of data available the field of machine learning has given rise to deep learning, a subset of machine learning algorithms that have shown extraordinary results in a variety of applications from natural language processing to computer vision. In the field of computer vision, these algorithms have greatly improved the state-of-the-art accuracy in tasks associated with object recognition such as detection. This thesis makes use of one of these algorithms, specifically the YOLO algorithm, as a basis in the development of a system capable of detecting objects laying inside a car cockpit. To this end a dataset is collected for the purpose of training the YOLO algorithm on this task. A comparative analysis of the detection performance of the YOLOv2 and YOLOv3 architectures is performed.Several experiments are performed by modifying the YOLOv3 architecture to attempt to improve its accuracy. Specifically tests are performed in regards to network size, and the multiple outputs present in this network. Explorative experiments are done in order to test the effect that parallel network might have on detection performance. Lastly tests are done to try to find an optimal learning rate and batch size for our dataset on the new architectures.
Com o crescente poder computacional que temos à nossa disposição e o aumento da quantidade dados a que temos acesso o campo de machine learning deu origem ao deep learning um subconjunto de algoritmos de machine learning que têm demonstrado resultados extraordinários numa variedade de aplicações desde processamento de linguagens naturais a visão por computador. No campo de visão por computador estes algoritmos têm levado a enormes progressos na correção de sistemas de deteção de objetos. Nesta tese usamos um destes algoritmos, especificament o YOLO, como base para desenvolver um sistema capaz de detetar objetos dentro de um carro. Dado isto um dataset é recolhido com o propósito de treinar o algoritmo YOLO nesta tarefa. Uma analise comparativa da correção dos algoritmos YOLOv2 e YOLOv3 ´e realizada. Várias técnicas relacionadas com a modificação da arquitetura YOLOv3 são exploradas para otimizar o sistema para o problema especifico de deteção a bordo de veículos. Especificamente testes são realizados no contexto de tamanho da rede e dos múltiplos outputs presentes nesta rede. Experiencias exploratórias são realizadas de forma a testar o efeito que redes parallelas podem ter na correção dos algoritmos. Por fim testes são feitos para tentar encontrar learning rates e batch sizes apropriados para o nosso dataset nas novas arquiteturas.
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Liu, Chun-Yu, and 劉峻瑜. "Implementation of Fruit Quality Classification System using YOLO Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2chdzs.

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碩士
國立高雄科技大學
電子工程系
107
The thesis presents a proposed system that uses YOLO (You Only Look Once)-V3 algorithm, IOU (Intersection over Union) tracking method, and CNN (Convolutional Neural Network) classifier to identify the external quality of fruits. The system mainly uses the YOLO-V3 algorithm to perform the fruit detection process, uses the IOU tracking algorithm to track the designated fruits continuously, and identifies fruits during the tracking processes. It can pick up good fruits through controlling the switched gap of conveying platform. It performs the software programs on the Jetson TX2 embedded development platform and uses the STM32 processor to control the switched gap. The proposed system can detect small and round fruits under an effective development process. To improve the efficiency of system, a graphic user interface is also designed to control , collect data, evaluate models,and monitor the entire system operation. The experimental results show that our proposed system can achieve up to 88% of the accuracy rate, 75% of the mean Average Precision (mAP) after testing 4,500 images of fruits.
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Yu, Wei-Chih, and 余韋志. "The object detection of moving ground vehicles using YOLO algorithm on UAV." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qqwcjc.

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碩士
義守大學
機械與自動化工程學系
106
When people talk about the definition of Computer Vision, the first thing that comes to mind is the image classification. Previous researches illustrated that image classification is one of the most basic tasks of computer vision. However, based on the basis of image classification, there are more complicated and interesting tasks, such as: object detection, object location, image segmentation...etc in computer vision. The object detection is a practical and challenging task, which can be regarded as a combination of image classification and object location, given a complicated target detection system. To identify the selected object from various targets in the picture (target detection system), and to give the precise location of the target demonstrated the target detection is more complicated than the classification task. A practical application scenario of target detection is autonomous cars and Unmanned Aerial Vehicle (UAV). The aim of this research is to develop an object detection system on a UAV. The developed system is capable to real-time capture the wanted target on the wall and moving target on the ground using remote control system. A series of comprehensive tests has been conducted based on the YOLO algorithm in this paper.
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Books on the topic "YOLO ALGORITHMS"

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Tovey, Craig A. A polynomial-time algorithm for computing the yolk in fixed dimension. Monterey, Calif: Naval Postgraduate School, 1991.

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Book chapters on the topic "YOLO ALGORITHMS"

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Sai Venu Prathap, K., D. Srinivasulu Reddy, S. Madhusudhan, and S. Mohammed Mazharr. "Intelligent Traffic Light System Using YOLO." In Algorithms for Intelligent Systems, 95–107. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_9.

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Gandhi, Jimit, Purvil Jain, and Lakshmi Kurup. "YOLO Based Recognition of Indian License Plates." In Algorithms for Intelligent Systems, 411–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_39.

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Ranjan, Ashish, Sunita Dhavale, and Suresh Kumar. "YOLO Algorithms for Real-Time Fire Detection." In Data Management, Analytics and Innovation, 537–53. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_40.

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Santhosh Kumar, C., K. Amritha Devangana, P. L. Abirami, M. Prasanna, and S. Hari Aravind. "Identification and Classification of Skin Diseases with Erythema Using YOLO Algorithm." In Algorithms for Intelligent Systems, 595–605. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_49.

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Ghosh, Rajib. "A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions." In Algorithms for Intelligent Systems, 45–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_5.

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Gali, Manoj, Sunita Dhavale, and Suresh Kumar. "Real-Time Image Based Weapon Detection Using YOLO Algorithms." In Communications in Computer and Information Science, 173–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12641-3_15.

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Jain, Shilpa, S. Indu, and Nidhi Goel. "Comparative Analysis of YOLO Algorithms for Intelligent Traffic Monitoring." In Proceedings on International Conference on Data Analytics and Computing, 159–68. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3432-4_13.

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Gonzalez, Dibet Garcia, João Carias, Yusbel Chávez Castilla, José Rodrigues, Telmo Adão, Rui Jesus, Luís Gonzaga Mendes Magalhães, et al. "Evaluating Rotation Invariant Strategies for Mitosis Detection Through YOLO Algorithms." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 24–33. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32029-3_3.

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Wang, Aibin, Youshi Ye, Yu Peng, Dezheng Zhang, Zhihong Yan, and Dong Wang. "A Low-Latency Hardware Accelerator for YOLO Object Detection Algorithms." In Lecture Notes in Computer Science, 265–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7872-4_15.

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Wyawahare, Medha, Jyoti Madake, Agnibha Sarkar, Anish Parkhe, Archis Khuspe, and Tejas Gaikwad. "Crop-Weed Detection, Depth Estimation and Disease Diagnosis Using YOLO and Darknet for Agribot: A Precision Farming Robot." In Algorithms for Intelligent Systems, 57–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_5.

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Conference papers on the topic "YOLO ALGORITHMS"

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Zhang, Ce, and Azim Eskandarian. "A Comparative Analysis of Object Detection Algorithms in Naturalistic Driving Videos." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69975.

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Abstract Intelligent vehicle research has been rapidly developing. Object detection is one of the most critical study areas for intelligent vehicle’s driving safety. This paper conducts a comparative analysis for two popular real-time object detection algorithms: Yolo-v4 and deconvolutional single shot multibox detector (DSSD) under a naturalistic driving environment. An 80-classes COCO dataset trains each neural network at first, then fine-tuned by the BDD100k dataset. The detection results are compared by True/False Positive Results, Precision-Recall Curve, and average precision @ intersection of union 50 and average precision @ intersection of union 75 results. According to the analysis results, the Yolo-v4 outperforms the DSSD algorithm in bad weather, nighttime conditions, and small object detections. The Yolo-v4 and DSSD mean average precision for the BDD dataset is 22.63 and 11.86, respectively. The Yolo-v4 precision is 90.81% better than the DSSD algorithm, proving that the Yolo-v4 is a better fit for real-world driving environment studies.
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Gillani, Ismat Saira, Muhammad Rizwan Munawar, Muhammad Talha, Salman Azhar, Yousra Mashkoor, Muhammad Sami uddin, and Usama Zafar. "Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey." In 8th International Conference on Artificial Intelligence and Fuzzy Logic System (AIFZ 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121602.

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YOLOv7 algorithm have taken the object detection domain by the storm as its real-time object detection capabilities out ran all other previous algorithms both in accuracy and speed [1]. YOLOv7 advances the state of the art results in object detection by inferring more quickly and accurately than its contemporaries. In this paper, we are going to present our work of implementing this SOTA deep learning model on a soccer game play video to detect the players and football. As the result, it detected the players, football and their movement in real time. We also analyzed and compared the YOLOv7 results against its previous versions including YOLOv4, YOLOv5 and YOLO-R. The code is available at: https://github.com/RizwanMunawar/YOLO-RX57-FPS-Comparision
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Bohong, Liu, and Wang Xinpeng. "Garbage Detection Algorithm Based on YOLO v3." In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744738.

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Li, Huiming, Shuo Zhou, Yuyuan Du, Qingliang Zou, and Shoufeng Tang. "Research on Robotic Arm Based on YOLO." In 2022 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI). IEEE, 2022. http://dx.doi.org/10.1109/ahpcai57455.2022.10087753.

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Huang, Jubin, Zexuan Guo, Xuebin Hong, Haohai Wu, and Zhe Lin. "UAV image object detection network based on Yolo." In 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), edited by Kannimuthu Subramaniam and Pavel Loskot. SPIE, 2023. http://dx.doi.org/10.1117/12.3005996.

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Pan, Zhai. "Research on Improved Yolo on Garbage Classification Task." In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744865.

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Dodia, Ayush, and Sumit Kumar. "A Comparison of YOLO Based Vehicle Detection Algorithms." In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1). IEEE, 2023. http://dx.doi.org/10.1109/icaia57370.2023.10169773.

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Tong, Bingshen, and Menglin Zhang. "Comparison of YOLO Series Algorithms in Mask Detection." In 2023 International Workshop on Intelligent Systems (IWIS). IEEE, 2023. http://dx.doi.org/10.1109/iwis58789.2023.10284631.

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Shen, Zhichao, and Zhiheng Zhao. "Improved lightweight peanut detection algorithm based on YOLO v3." In 2021 International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/caibda53561.2021.00043.

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Li, Chuan, Manming Shu, Ling Du, Haoyue Tan, and Lang Wei. "Design of Automatic Recycling Robot Based on YOLO Target Detection." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00059.

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Reports on the topic "YOLO ALGORITHMS"

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Tovey, Craig A. A Polynomial-Time Algorithm for Computing the Yolk in Fixed Dimension. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada240060.

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