Improved YOLOv5 Traffic Light Real-Time Detection Robust Algorithm

Abstract

Traffic light detection algorithm, a critical procedure for realization of automatic driving, is directly related to the driving safety of intelligent vehicles. However, due to the small size of traffic lights and complicated environment, the algorithm research meets plenty of difficulties. This paper puts forward a traffic light detection algorithm based on optimized YOLOv5. Firstly, it uses a visible label ratio to determine the model input. Secondly, the ACBlock structure is introduced to increase the feature extraction ability of the backbone network; the SoftPool is designed to reduce the sample loss of the backbone network and the DSConv convolution kernel is used to reduce the model parameters. Finally, a memory feature fusion network is designed to efficiently utilize high level semantic information and low level features. As a result, the improvement of model input and backbone network directly improves the feature extraction ability of the model in complex environment; the improvement of feature fusion network enables the model to make full use of feature information and increase the accuracy of target positioning and boundary regression. Experimental results show that, the proposed algorithm achieves 74.3% AP and 111 frame/s detection speed on BDD100K, which is 11.0 percentage points higher than the AP of YOLOv5. In Bosch data set, 84.4% AP and 126 frame/s detection speed are obtained, which is 9.3 percentage points higher than the AP of YOLOv5. The robustness test results show that the proposed algorithm has significantly improved the detection ability of tar-gets in a variety of complex environments, and the robustness is increased to achieve high-precision real-time detection.

Keywords

• |traffic light detection|yolov5|memory feature fusion network|bdd100k|real-time detection