Gotowa bibliografia na temat „YOLO ALGORITHMS”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „YOLO ALGORITHMS”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "YOLO ALGORITHMS"
Wan, Chengjuan, Yuxuan Pang i Shanzhen Lan. "Overview of YOLO Object Detection Algorithm". International Journal of Computing and Information Technology 2, nr 1 (25.08.2022): 11. http://dx.doi.org/10.56028/ijcit.1.2.11.
Pełny tekst źródłaKadhum, Aseil Nadhum, i Aseel Nadhum Kadhum. "Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic". June-July 2023, nr 34 (29.07.2023): 27–35. http://dx.doi.org/10.55529/jipirs.34.27.35.
Pełny tekst źródłaZhou, Xuan, Jianping Yi, Guokun Xie, Yajuan Jia, Genqi Xu i Min Sun. "Human Detection Algorithm Based on Improved YOLO v4". Information Technology and Control 51, nr 3 (23.09.2022): 485–98. http://dx.doi.org/10.5755/j01.itc.51.3.30540.
Pełny tekst źródłaLiu, Tao, Bo Pang, Lei Zhang, Wei Yang i 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, nr 7 (7.07.2021): 753. http://dx.doi.org/10.3390/jmse9070753.
Pełny tekst źródłaChen, Xin, Peng Shi i Yi Hu. "A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C". Journal of Marine Science and Engineering 11, nr 7 (24.07.2023): 1475. http://dx.doi.org/10.3390/jmse11071475.
Pełny tekst źródłaCong, Xiaohan, Shixin Li, Fankai Chen, Chen Liu i Yue Meng. "A Review of YOLO Object Detection Algorithms based on Deep Learning". Frontiers in Computing and Intelligent Systems 4, nr 2 (25.06.2023): 17–20. http://dx.doi.org/10.54097/fcis.v4i2.9730.
Pełny tekst źródłaKarmakar, Malay. "Face Recognition Technique using YOLO V5 Algorithm". International Research Journal of Computer Science 10, nr 03 (31.03.2023): 04–12. http://dx.doi.org/10.26562/irjcs.2023.v1002.01.
Pełny tekst źródłaGao, Ruizhen, Shuai Zhang, Haoqian Wang, Jingjun Zhang, Hui Li i Zhongqi Zhang. "The Aeroplane and Undercarriage Detection Based on Attention Mechanism and Multi-Scale Features Processing". Mobile Information Systems 2022 (19.09.2022): 1–12. http://dx.doi.org/10.1155/2022/2582288.
Pełny tekst źródłaLiu, Jiayi, Xingfei Zhu, Xingyu Zhou, Shanhua Qian i Jinghu Yu. "Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm". Electronics 11, nr 10 (13.05.2022): 1561. http://dx.doi.org/10.3390/electronics11101561.
Pełny tekst źródłaLi, Zhuang, Jianhui Yuan, Guixiang Li, Hao Wang, Xingcan Li, Dan Li i Xinhua Wang. "RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO". Sensors 23, nr 14 (14.07.2023): 6414. http://dx.doi.org/10.3390/s23146414.
Pełny tekst źródłaRozprawy doktorskie na temat "YOLO ALGORITHMS"
Marmayohan, Nivethan, i 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.
Pełny tekst źródłaObject 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.
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/.
Pełny tekst źródłaARYA, DEEPRAJ. "POTHOLE DETECTION". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20452.
Pełny tekst źródłaFarinha, João Simões. "In-vehicle object detection with YOLO algorithm". Master's thesis, 2018. http://hdl.handle.net/1822/64273.
Pełny tekst źródłaWith 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.
Liu, Chun-Yu, i 劉峻瑜. "Implementation of Fruit Quality Classification System using YOLO Algorithm". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2chdzs.
Pełny tekst źródła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.
Yu, Wei-Chih, i 余韋志. "The object detection of moving ground vehicles using YOLO algorithm on UAV". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qqwcjc.
Pełny tekst źródła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.
Książki na temat "YOLO ALGORITHMS"
Tovey, Craig A. A polynomial-time algorithm for computing the yolk in fixed dimension. Monterey, Calif: Naval Postgraduate School, 1991.
Znajdź pełny tekst źródłaCzęści książek na temat "YOLO ALGORITHMS"
Sai Venu Prathap, K., D. Srinivasulu Reddy, S. Madhusudhan i S. Mohammed Mazharr. "Intelligent Traffic Light System Using YOLO". W Algorithms for Intelligent Systems, 95–107. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_9.
Pełny tekst źródłaGandhi, Jimit, Purvil Jain i Lakshmi Kurup. "YOLO Based Recognition of Indian License Plates". W Algorithms for Intelligent Systems, 411–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_39.
Pełny tekst źródłaRanjan, Ashish, Sunita Dhavale i Suresh Kumar. "YOLO Algorithms for Real-Time Fire Detection". W Data Management, Analytics and Innovation, 537–53. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_40.
Pełny tekst źródłaSanthosh Kumar, C., K. Amritha Devangana, P. L. Abirami, M. Prasanna i S. Hari Aravind. "Identification and Classification of Skin Diseases with Erythema Using YOLO Algorithm". W Algorithms for Intelligent Systems, 595–605. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_49.
Pełny tekst źródłaGhosh, Rajib. "A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions". W Algorithms for Intelligent Systems, 45–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_5.
Pełny tekst źródłaGali, Manoj, Sunita Dhavale i Suresh Kumar. "Real-Time Image Based Weapon Detection Using YOLO Algorithms". W 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.
Pełny tekst źródłaJain, Shilpa, S. Indu i Nidhi Goel. "Comparative Analysis of YOLO Algorithms for Intelligent Traffic Monitoring". W 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.
Pełny tekst źródłaGonzalez, Dibet Garcia, João Carias, Yusbel Chávez Castilla, José Rodrigues, Telmo Adão, Rui Jesus, Luís Gonzaga Mendes Magalhães i in. "Evaluating Rotation Invariant Strategies for Mitosis Detection Through YOLO Algorithms". W 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.
Pełny tekst źródłaWang, Aibin, Youshi Ye, Yu Peng, Dezheng Zhang, Zhihong Yan i Dong Wang. "A Low-Latency Hardware Accelerator for YOLO Object Detection Algorithms". W Lecture Notes in Computer Science, 265–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7872-4_15.
Pełny tekst źródłaWyawahare, Medha, Jyoti Madake, Agnibha Sarkar, Anish Parkhe, Archis Khuspe i Tejas Gaikwad. "Crop-Weed Detection, Depth Estimation and Disease Diagnosis Using YOLO and Darknet for Agribot: A Precision Farming Robot". W Algorithms for Intelligent Systems, 57–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_5.
Pełny tekst źródłaStreszczenia konferencji na temat "YOLO ALGORITHMS"
Zhang, Ce, i Azim Eskandarian. "A Comparative Analysis of Object Detection Algorithms in Naturalistic Driving Videos". W ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69975.
Pełny tekst źródłaGillani, Ismat Saira, Muhammad Rizwan Munawar, Muhammad Talha, Salman Azhar, Yousra Mashkoor, Muhammad Sami uddin i Usama Zafar. "Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey". W 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.
Pełny tekst źródłaBohong, Liu, i Wang Xinpeng. "Garbage Detection Algorithm Based on YOLO v3". W 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744738.
Pełny tekst źródłaLi, Huiming, Shuo Zhou, Yuyuan Du, Qingliang Zou i Shoufeng Tang. "Research on Robotic Arm Based on YOLO". W 2022 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI). IEEE, 2022. http://dx.doi.org/10.1109/ahpcai57455.2022.10087753.
Pełny tekst źródłaHuang, Jubin, Zexuan Guo, Xuebin Hong, Haohai Wu i Zhe Lin. "UAV image object detection network based on Yolo". W 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), redaktorzy Kannimuthu Subramaniam i Pavel Loskot. SPIE, 2023. http://dx.doi.org/10.1117/12.3005996.
Pełny tekst źródłaPan, Zhai. "Research on Improved Yolo on Garbage Classification Task". W 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744865.
Pełny tekst źródłaDodia, Ayush, i Sumit Kumar. "A Comparison of YOLO Based Vehicle Detection Algorithms". W 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.
Pełny tekst źródłaTong, Bingshen, i Menglin Zhang. "Comparison of YOLO Series Algorithms in Mask Detection". W 2023 International Workshop on Intelligent Systems (IWIS). IEEE, 2023. http://dx.doi.org/10.1109/iwis58789.2023.10284631.
Pełny tekst źródłaShen, Zhichao, i Zhiheng Zhao. "Improved lightweight peanut detection algorithm based on YOLO v3". W 2021 International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/caibda53561.2021.00043.
Pełny tekst źródłaLi, Chuan, Manming Shu, Ling Du, Haoyue Tan i Lang Wei. "Design of Automatic Recycling Robot Based on YOLO Target Detection". W 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00059.
Pełny tekst źródłaRaporty organizacyjne na temat "YOLO ALGORITHMS"
Tovey, Craig A. A Polynomial-Time Algorithm for Computing the Yolk in Fixed Dimension. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1991. http://dx.doi.org/10.21236/ada240060.
Pełny tekst źródła