Abstract: Traffic sign detection has always been a hot topic in the field of autonomous driving. In deep learning algorithms, YOLOv3 and Faster R-CNN have obtained excellent object detection performance, but when detecting small targets, there are cases of missed detection. In order to accurately and quickly identify small targets in traffic sign detection, this paper proposes a lightweight YOLOv3 traffic sign detection algorithm. Multi-scale feature maps are obtained through convolutional neural networks while using both shallow and deep feature extraction. Deep features can effectively keep the detection accuracy from falling, and shallow features can effectively improve the accuracy of small target detection tasks. The model is compressed by the pruning algorithm, the trained model is sparsely trained, some unimportant convolution kernel channels are deleted, and the pruned model is fine-tuning to maintain the balance of parameters in the model file. The experimental results show that the accuracy of the model is improved by 2.3% by extracting the multi-scale feature map, the weight of the model is reduced by 70% by compressing the model with the pruning algorithm, and the detection time of the model is saved by 90%. Therefore, a lightweight traffic sign detection model with stronger robustness can be deployed on mobile embedded devices, without taking up large GPU computing resources but improving detection efficiency.

Key words: convolutional neural network, traffic signs, small object detection, multi-scale feature map, model compression