Smart City Gnosys

Smart city article details

Title	Research On Traffic Sign Recognition Algorithms For Autonomous Vehicles In Smart City Traffic Systems
ID_Doc	45887
Authors	Wang C.
Year	2024
Published	Proceedings of SPIE - The International Society for Optical Engineering, 13256
DOI	http://dx.doi.org/10.1117/12.3037841
Abstract	With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. This study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. The images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. The research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.
Author Keywords	Autonomous Vehicle; Smart City; Traffic Sign Recognition; YOLOv5

Similar Articles

Id	Similarity	Authors	Title	Published
17855	0.905	Faraji P.H.; Tohidypour H.R.; Wang Y.; Nasiopoulos P.; Ren S.; Rizvi A.; Feng C.; Pourazad M.T.; Leung V.C.M.	Deep Learning Based Street Parking Sign Detection And Classification For Smart Cities	GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good (2021)
58662	0.903	Ayachi R.; Afif M.; Said Y.; Abdelali A.B.	Traffic Sign Detection For Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network	Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges (2022)
32396	0.9	Hegde S.K.; Dharmalingam R.; Kannan S.	Intelligent German Traffic Sign And Road Barrier Assist For Autonomous Driving In Smart Cities	Multimedia Tools and Applications, 83, 22 (2024)
2473	0.9	Smitha Shekar B.; Harish G.	A Machine Learning Model For Detection And Recognition Of Traffic Signs	2021 International Conference on Intelligent Technologies, CONIT 2021 (2021)
18852	0.9	Ding W.; Li W.; Ma D.	Design Of Improved Road Sign Recognition System Based On Deep Learning	Proceedings of SPIE - The International Society for Optical Engineering, 12552 (2023)
11302	0.897	Srivastava V.; Mishra S.; Gupta N.	Automatic Detection And Categorization Of Road Traffic Signs Using A Knowledge-Assisted Method	Procedia Computer Science, 218 (2022)
30032	0.894	Dhawan K.; Srinivasa Perumal R.; Nadesh R.K.	Identification Of Traffic Signs For Advanced Driving Assistance Systems In Smart Cities Using Deep Learning	Multimedia Tools and Applications, 82, 17 (2023)
44408	0.893	Khalifa A.A.; Alayed W.M.; Elbadawy H.M.; Sadek R.A.	Real-Time Navigation Roads: Lightweight And Efficient Convolutional Neural Network (Le-Cnn) For Arabic Traffic Sign Recognition In Intelligent Transportation Systems (Its)	Applied Sciences (Switzerland), 14, 9 (2024)
58661	0.893	Ayachi R.; Afif M.; Said Y.; Abdelali A.B.	Traffic Sign Detection For Green Smart Public Transportation Vehicles Based On Light Neural Network Model	Green Energy and Technology (2022)
50341	0.889	Dawam E.S.; Feng X.	Smart City Lane Detection For Autonomous Vehicle	Proceedings - IEEE 18th International Conference on Dependable, Autonomic and Secure Computing, IEEE 18th International Conference on Pervasive Intelligence and Computing, IEEE 6th International Conference on Cloud and Big Data Computing and IEEE 5th Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020 (2020)