Journal of System Simulation
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%.
Recommended Citation
Zheng, Lanyue and Zhang, Yujie
(2025)
"Traffic Signal Detection Based on Improved YOLOv7,"
Journal of System Simulation: Vol. 37:
Iss.
4, Article 14.
DOI: 10.16182/j.issn1004731x.joss.23-1562
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss4/14
First Page
993
Last Page
1007
CLC
TP183
Recommended Citation
Zheng Lanyue, Zhang Yujie. Traffic Signal Detection Based on Improved YOLOv7[J]. Journal of System Simulation, 2025, 37(4): 993-1007.
DOI
10.16182/j.issn1004731x.joss.23-1562
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