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Title	Traffic Signal Detection And Recognition From Real-Scenes Using Yolo
ID_Doc	58671
Authors	Bahadure N.B.; Patil P.D.; Birewar R.; Nayyar P.; Shrivastav A.; Oberoi M.
Year	2023
Published	2023 IEEE Engineering Informatics, EI 2023
DOI	http://dx.doi.org/10.1109/IEEECONF58110.2023.10520597
Abstract	This research aims to create a robust system for detecting traffic lights using computer vision techniques. The proposed method uses image processing techniques and deep learning algorithms to detect and recognize traffic lights in real-time. Transfer learning is used to fine-tune a deep neural network model using a comprehensive dataset of annotated traffic light images. The trained model uses cutting-edge object detection algorithms to locate and identify traffic lights within input frames. Picture handling methods are then applied to refine the recognition results and dispose of bogus up-sides. The framework consolidates worldly examination to follow traffic signals across outlines, guaranteeing solid acknowledgment even in testing situations. Execution assessment utilizing genuine world datasets exhibits the framework's exactness, effectiveness, and potential for improving street well-being and keen transportation frameworks. Autonomous driving, traffic monitoring, and smart city infrastructure can all benefit from this research contribution to computer vision. The newly developed system for detecting traffic lights has the potential to make transportation systems safer and more effective. © 2023 IEEE.
Author Keywords	deep learning; traffic light; You only look once (YOLO)

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