Abstract
In the practical application of traffic target detection, it is difficult to ensure the detection accuracy of multi-scale traffic signs in real-time detection because the scale of the target varies greatly. In this paper, an improved method based on YOLOv5s is proposed to address the accuracy problem of multi-scale targets in real-time traffic detection by adapting its data enhancement at the input and localisation loss at the output respectively. The original data enhancement method is improved to solve the problem of target loss in a small range to a certain extent. In addition, an α-FEIOU algorithm is proposed to replace the original CIoU algorithm to improve the accuracy of real-time target detection. The results show that the proposed method shows good performance in different scenarios and its effectiveness is confirmed. At the same time, it has strong robustness and provides a certain reference for the application of deep learning in the field of traffic target detection.
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