The Research on Lightweight Traffic Sign Recognition Algorithm Based on Improved YOLOv5 Model

Abstract

Traffic sign detection is an important research direction in object detection, which has been widely used in intelligent transportation system, driving assistance, automatic driving and other fields. In practical applications, traffic sign detection algorithms are required to complete detection and recognition tasks quickly and accurately, which requires the algorithm model to be lightweight to meet the deployment conditions. Aiming at the existing traffic sign detection problems, a lightweight traffic sign detection network based on YOLOv5s model was constructed, which improved the detection performance of the network model on the premise of guaranteeing the computing speed. In order to ensure lightweight, YOLOv5s model was selected. Firstly, Dense CSP Module (DCM) was designed to enhance the effect of feature fusion. At the same time, the feature pyramid is improved, and reduced the number of parameters in the model. Experimental results show that compared with the original algorithm, the detection efficiency of the proposed algorithm is improved by 5.28%, and the experimental results on multiple data sets show obvious improvement effect. This is a lightweight model that works well in the area of traffic sign detection.