关键词: 目标检测, 轻量级, GhostNet, 知识蒸馏, 密集空洞空间金字塔池化

Abstract: In view of the existing traffic signal detection algorithms, which require a large amount of computation and model, are difficult to deploy at the embedded end, and are difficult to detect long-distance traffic signals with a high miss rate, this paper designs a lightweight traffic signal detection algorithm for the embedded end, which aims at lightweight and real-time requirements. GhostNet network Ghost module and Ghost bottleneck layer structure are adopted to reduce the number of model parameters and improve the detection speed. To solve the feature similarity problem, the weighted bidirectional feature pyramid network is used to make the algorithm more sensitive to the target. The dense void space pyramid pool is used to optimize the extraction of global context information. Aiming at the problem of small target recognition, the information extraction of small target is enhanced through the improvement of multi-scale detection. Finally, the learning ability of the model is improved through knowledge distillation, and the detection performance is improved. Experimental results show that the recognition accuracy and recall rate of traffic lights by this detection algorithm reach 97.0% and 99% respectively, 2.7 and 3 percentage points higher than that of YOLOv5s. The model size is reduced to 8.06 MB, 58% of that of YOLOv5s, and the recognition rate is increased from 51 frames per second to 56 frames per second. Through testing in the embedded terminal, the improved algorithm can recognize the traffic signal in real time and accurately.

Key words: object detection, ?lightweight, ?GhostNet, ?knowledge of distillation, dense atrous spatial pyramid pooling