Traffic Signal Detection Based on Improved YOLOv7

doi:10.16182/j.issn1004731x.joss.23-1562

Abstract

Abstract:

An improved YOLOv7 is proposed to address the problem of low recognition accuracy in general object detection algorithms for traffic signal detection. The algorithm removes the 20×20 detection scale and adds a 160×160 detection scale to increase shallow features while making the model lightweight. It combines the bi-level routing attention (BRA) proposed in BiFormer with axial attention, and innovatively proposes axially-guided BRA (ABRA). This module is specifically designed for the characteristics of traffic signal positions. To address the issue of object size sensitivity to the IoU metric, the normalized wasserstein distance (NWD) measurement is introduced to improve object location loss and objectness loss. Experimental results show that on the S²TLD dataset, the improved YOLOv7 algorithm achieves a mAP value of 97.7%, which is an improvement of 11.4% over the original YOLOv7. The detection speed is increased by 90 frames/s, and the computational complexity is reduced by 4.5%.

Key words: traffic signal detection, attention mechanism, BiFormer, normalized wasserstein distance (NWD), YOLOv7

CLC Number:

TP183

Zheng Lanyue, Zhang Yujie. Traffic Signal Detection Based on Improved YOLOv7[J]. Journal of System Simulation, 2025, 37(4): 993-1007.

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类别	各类别平均精确度/%
红	95.5
绿	95.5
关	77.9
黄	76.2
总体	86.3

方法	平均精确度/ %	帧率/ (帧/s)	浮点运算量/G
YOLOv7_P1234	94.3	312	116.2
YOLOv7_P123	95.2	333	97.9

k值	平均精确度/%	浮点运算量/G
1	95.6	98.4
2	96.8	98.5
3	90.9	98.5
4	95.6	98.6
5	96.0	98.6

注意力模块	平均精确度/%
Vanilla Attention^[27]	93.9
BRA^[19]	88.1
ABRA	96.8

损失函数	平均精确度/%
原始损失函数	96.8
改进的损失函数	97.7