Traffic sign detection based on YOLOv8-s and BiFPN fusion

SHAO Yinchun; LUO Suyun; WEI Dan

doi:10.12299/jsues.24-0203

Volume 40 Issue 1

Mar. 2026

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Article Navigation > Journal of Shanghai University of Engineering Science > 2026 > 40(1): 88-94

SHAO Yinchun, LUO Suyun, WEI Dan. Traffic sign detection based on YOLOv8-s and BiFPN fusion[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 88-94. doi: 10.12299/jsues.24-0203

Citation:

SHAO Yinchun, LUO Suyun, WEI Dan. Traffic sign detection based on YOLOv8-s and BiFPN fusion[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 88-94. doi: 10.12299/jsues.24-0203

SHAO Yinchun, LUO Suyun, WEI Dan. Traffic sign detection based on YOLOv8-s and BiFPN fusion[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 88-94. doi: 10.12299/jsues.24-0203

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Traffic sign detection based on YOLOv8-s and BiFPN fusion

doi: 10.12299/jsues.24-0203

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-07-16
Available Online: 2026-05-27
Publish Date: 2026-03-01

Abstract

Abstract

Traffic sign detection plays a crucial role in driver assistance system, contributing significantly to driving safety and traffic efficiency. To address the challenges posed by the small size, wide variety and complex background interference of traffic signs, a traffic sign detection algorithm based on improved YOLOv8-s, named BTSD-YOLO, was proposed. By integrating the multi-level structure of the BiFPN, the multi-scale feature fusion capability of the model was enhanced. A small object detection layer was added to strengthen the detection capability for small-sized targets. The WIoU_v3 loss function was adopted to reduce harmful gradients generated by low-quality examples. The results indicate that the proposed algorithm can significantly enhance detection ability of small targets, and optimize the fusion mechanism of multi-scale information, effectively reducing false positive and false negative rates. Compared with the original YOLOv8-s algorithm, the improved algorithm achieves a 6.9% increase on the COCO accuracy evaluation index mAP@50 index, and a 6.0% increase on the mAP@50:95 metric, fully validating the effectiveness of the proposed improvement.
- traffic sign detection,
- YOLOv8-s,
- multi-scale feature fusion module,
- loss function

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References(17)

References

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