Traffic sign recognition based on YOLOv5

Fujin Hou; Yanqiang huo; Youfu Lu; Tao Li; Jian Li

doi:10.1117/12.2645910

23 August 2022 Traffic sign recognition based on YOLOv5

Fujin Hou, Yanqiang huo, Youfu Lu, Tao Li, Jian Li

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 1230510 (2022) https://doi.org/10.1117/12.2645910
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

With the development of intelligent transportation, traffic sign detection has become a very important task. Traffic sign detection requires the positioning and classification of traffic signs in the road environment. Due to the complexity and diversity of road environments, traffic signs can only take up a small proportion of videos or pictures, and common algorithms still show high false detection rate and high time and calculation overhead in practical application. So far, traffic sign detection is still a difficult task. At the same time, most traffic sign data sets used by algorithms at the present stage have unreasonable data structures or incomplete database types. Blind application of unstructured data sets easily lead to the difficulty of obtaining good results in model training. Therefore, this paper decided to carry out the traffic sign detection task based on the lightweight YOLOv5 (You Only Look Once) neural network model. In order to achieve excellent target detection effect, this paper adopts optimized Tsinghua-Tencent-100K (TT100K) data set to train the model. The experimental results show that the trained models mAP@0.5 and mAP@0.5:0.95 reach 59% and 40% respectively, which basically meet the requirements of application.

Citation Download Citation

Fujin Hou, Yanqiang huo, Youfu Lu, Tao Li, and Jian Li "Traffic sign recognition based on YOLOv5", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 1230510 (23 August 2022); https://doi.org/10.1117/12.2645910

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