M-YOLO: Traffic Sign Detection Algorithm Applicable to Complex Scenarios

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Abstract
Traffic signs can be seen everywhere in daily life. Traffic signs are symmetrical, and traffic sign detection is easily affected by distortion, distance, light intensity and other factors, which also increases the potential safety hazards of assisted driving in practical application. In order to solve this problem, a symmetrical traffic sign detection algorithm M-YOLO for complex scenes is proposed. The algorithm optimizes the delay problem by reducing the computational overhead of the network, and speeds up the speed of feature extraction. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in complex environments, such as scale and illumination changes. Experimental results on CCTSDB dataset containing traffic signs in complex scenes and HRRSD small target dataset show that M-YOLO algorithm has good detection performance. Compared with other algorithms, it has higher detection accuracy and detection speed. The test results in real complex scenes show that the detection effect of this algorithm is better than that of YOLOv5l algorithm, and M-YOLO algorithm can accurately detect the traffic signs that cannot be detected by YOLOv5l algorithm. Therefore, the algorithm proposed in this article can effectively improve the detection accuracy of traffic signs, is suitable for complex scenes, and has a good detection effect on small targets.
Funding Information
  • Natural Science Foundation of Xinjiang Uygur Autonomous Region (2020D01C047)

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