Affiliations: [a]
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
| [b]
Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei University of Technology, Hefei, China
Abstract: Deep learning has dominated the research field of traffic sign detection, but the traffic sign detection algorithms based on deep learning have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic and complex traffic scene images, and the images or the types of traffic signs provided by the public dataset used by the relevant algorithm cannot meet the situations encountered in realistic traffic scenes.To solve the above problems, this paper creates a new road traffic sign dataset, and based on the YOLOv4 algorithm, designs a multi-size feature extraction module and an enhanced feature fusion module to improve the algorithm’s ability to locate and classify traffic signs simultaneously, in view of the complexity of realistic traffic scene images and the large variation of traffic sign sizes in the images. The experimental results on the newly created dataset show that the improved algorithm achieves 83.63% mean Average Precision (mAP), which is higher than several major object detection algorithms based on deep learning for the same type of task at present. The newly created dataset in this paper is publicly available at https://github.com/zhang1018/Traffic-sign-dataset-for-public.
Keywords: Traffic sign detection and recognition, traffic sign datasets, autonomous driving, convolutional neural networks, intelligent traffic system
