Lightweight Traffic Sign Detection Network Based on Weak Semantic Segmentation

Aiming at the problems of slow speed and low accuracy in detecting high-resolution traffic sign images in existing networks, a lightweight traffic sign-detection network is proposed.On the basis of MobileNetv3-Large, this study optimizes the backbone of a YOLOv4 network, discards some time-consuming layers according to the characteristics of the dataset, changes the number of output channels of layers 8 and 14, and improves the attention mechanism of Squeeze and Excitation Network (SENet) in the basic module, so that the weight value of the output can more accurately represent the importance of the characteristics.This study adds a dynamic enhanced attachment based on weak semantic segmentation in front of the detection header, and uses its output as the spatial weight distribution to correct the active region, to avoid the problem of false detection and missed detection caused by the decline of extraction ability, and finally form a YOLOv4-SLite network.The sliding window clipping method is used to train and predict high-resolution images, to reduce the training time and increase the diversity of samples.The experimental results on the TT100K traffic sign dataset show that, compared with the YOLOv4 benchmark network, the mAP@0.5 of the YOLOv4-SLite network is lost by 0.2%, but the model size is reduced by 96.5%, and the response speed is increased by 227%.The balance of accuracy and speed achieved meets the expectation.