Detection of Traffic Sign Based on Improved YOLOv4
Zeliang Liu, Yasenjiang Musha, Huawen Wu · 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) · 2022
To improve safe driving and reduce the mental fatigue of drivers, accurate and fast access to traffic sign guidance information in road scenes is needed. This work proposes a traffic sign detection method based on improved YOLOv4. First, the MLP architecture is combined with a convolutional neural network to enhance the network's ability to detect objects. Second, the same-level connection is added within PANet to enhance the feature information extracted by the network. Experiments show that the improved YOLOv4 algorithm can improve the detection accuracy of 416×416 sample images from 72.95% to 78.84%, and still meet the real-time detection requirements. After comparing our method with similar algorithms, the experimental results show that our algorithm has certain advantages.