Improved traffic sign detection algorithm of YOLOv3

Gong Rui; Zhao Xiaohu; Huo Yu

doi:10.1117/12.2682516

20 June 2023 Improved traffic sign detection algorithm of YOLOv3

Gong Rui, Zhao Xiaohu, Huo Yu

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 1271507 (2023) https://doi.org/10.1117/12.2682516
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

A traffic sign detection algorithm based on improved YOLOv3 in natural environment is constructed to address the problems of the existing traffic sign detection model with a large number of small-sized traffic sign, low accuracy rate and slow recognition speed for traffic sign with small object. Firstly, model compression is performed on the network structure, using the network pruning method to reduce the model size and improve the operation speed; Secondly, one scale detection structure is added on the basis of YOLOv3 network structure, and four-scale detection structure are used to fully enable the network to obtain more feature information of small traffic sign and effectively improve the detection rate of small object traffic sign; Finally, the K-means++algorithm is used to re-cluster and calculate the size of the bounding box of the object in the TT100K data set to generate a smaller size prior bounding box, which improves the recognition accuracy of the model for small target traffic sign. The experimental results on TT100K traffic sign data set show that the proposed method has better accuracy and recall than the original YOLOv3 model.

Citation Download Citation

Gong Rui, Zhao Xiaohu, and Huo Yu "Improved traffic sign detection algorithm of YOLOv3", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 1271507 (20 June 2023); https://doi.org/10.1117/12.2682516

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