Computer and Modernization ›› 2025, Vol. 0 ›› Issue (02): 94-99.doi: 10.3969/j.issn.1006-2475.2025.02.013

Previous Articles     Next Articles

Improved Traffic Sign Detection Algorithm of YOLOv7

  

  1. (Software School, North University of China, Taiyuan 030051, China)
  • Online:2025-02-28 Published:2025-02-28

Abstract: In view of the problems such as error detection and missing detection in the small pixel proportion of traffic signs, a traffic sign detection algorithm based on improved YOLOv7 is proposed. In YOLOv7, we introduce the small target detection layer and delete the large target detection layer to better meet the detection needs of small targets. In the backbone network, we introduce the EMA attention mechanism to improve the feature extraction capability of the model for multi-scale targets with reduced computational overhead. At the same time, ELAN-RPC module is constructed to replace the original ELAN, reduce the network calculation and improve the network reasoning speed. In addition, RFE module is introduced in the feature fusion layer to make better use of the details of the shallow feature map and improve the ability of subsequent top-down feature fusion. Experimental results show that the mAP of the improved YOLOv7 on TT100K dataset reaches 89.6%, which is 5.8 percentage points higher than that of the original algorithm, while the number of parameters is reduced by 37%, achieving the detection effect of fewer parameters and higher precision.

Key words:  , traffic sign detection; YOLOv7; attention mechanism; feature fusion; deep learning

CLC Number: