Research on YOLOv4-tiny traffic sign detection algorithm with attention mechanism

In the process of traffic sign detection, the small and dense traffic signs which are influenced by bad weather, similar interference and other natural environment, lead to poor detection performance. To solve these problems, this paper proposes a target detection network based on improved YOLOv4-tiny, which improves the original YOLOv4-tiny backbone extraction network through the attention mechanism based on channel, and obtains a new backbone extraction network to increase the interpretability of neural network. K-means clustering algorithm is used to calculate the anchor value which is suitable for the experimental dataset. The experiment results show that, compared with the original model, the mAP value of the improved model is increased by 1.81% and our model can effectively improve the performance of small target detection. It only needs 0.8s to get the traffic sign detection results, which can meet the real-time requirements of practical application scenarios.