Optimization of YOLOv8 Traffic Sign Object Detection Based on BiFPN Feature Pyramid and CBAM Attention Module

Xinyan Wu

doi:10.54254/2755-2721/2025.26120

Applied and Computational EngineeringOpen access

Optimization of YOLOv8 Traffic Sign Object Detection Based on BiFPN Feature Pyramid and CBAM Attention Module

Research Article

Open Access

Optimization of YOLOv8 Traffic Sign Object Detection Based on BiFPN Feature Pyramid and CBAM Attention Module

Xinyan Wu ^1*

¹ College of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian, 350116, China

^*Corresponding author: jinriyeye@163.com

Published on 20 August 2025

ACE Vol.176

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-239-3

ISBN (Online): 978-1-80590-240-9

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Abstract

This study proposes the integration of BiFPN feature pyramid and CBAM attention module in YOLOv8 to enhance the robustness of traffic sign and signal detection, based on the urgent need for urban road safety and autonomous driving. The experiment was validated on a test set of 801 images and 944 targets, and the overall precision of the model reached 0.739, Recall 0.654，mAP50 0.723，mAP50-95 0.631， Significantly better than the baseline, with improvements of 5.2%, 1%, 2.8%, and 2.15% respectively, confirming that the improvement strategy effectively reduces false positives and improves localization classification consistency. The subdivision results show that the Stop logo achieves almost zero missed detections due to its high contrast and regular shape, with Precision and mAP50 both approaching 1; The mAP50 of the three speed limits of 20, 60, and 70 km/h all exceeded 0.82 under sufficient sample conditions, and remained above 0.75 on the stricter mAP50-95 index, indicating good generalization to scale and lighting changes; Although data is scarce for speed limits of 100 and 120 km/h, mAP50 still reaches 0.77 and 0.85, indicating that the network has fully learned the common features of circular speed limit signs; In contrast, signal lights such as Red Light have a small scale and complex background, with mAP50-95 less than 0.35 and low recall, making them a key focus for future optimization. Overall, the current model has matured for high contrast and regular shape signs. The next step should be to focus on improving the recall rate of small sample categories and traffic lights through difficult case mining, multi-scale training, and targeted data augmentation. The gap between mAP50 and mAP50-95 should be narrowed at higher IoU thresholds to meet the high reliability requirements of real road scenes. This study not only validates the effectiveness of BiFPN+CBAM in traffic sign detection, but also provides a reference for improving low sample category and small object detection, which has positive significance for promoting the safe implementation of intelligent transportation systems and autonomous driving.

Keywords:

YOLOv8, BiFPN feature pyramid, CBAM attention module, traffic sign detection.

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References

[1]. Taslim Arif, et al."Vision-based real-time traffic flow monitoring system for road intersections in Dhaka city."Applied Intelligence 55.11(2025): 800-800.

[2]. Amir T. Mohamed, et al."Integrating EnlightenGAN for enhancing car logo detection under challenging lighting conditions."Multimedia Tools and Applications prepublish(2025): 1-28.

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References

[1]. Taslim Arif, et al."Vision-based real-time traffic flow monitoring system for road intersections in Dhaka city."Applied Intelligence 55.11(2025): 800-800.

[2]. Amir T. Mohamed, et al."Integrating EnlightenGAN for enhancing car logo detection under challenging lighting conditions."Multimedia Tools and Applications prepublish(2025): 1-28.

[3]. Wang, Xueqiu, et al. "BL-YOLOv8: An improved road defect detection model based on YOLOv8." Sensors 23.20 (2023): 8361.

[4]. Aswath S., et al."Smart Car Parking System with Online Reservation."Procedia Computer Science 258.(2025): 2777-2786.

[5]. XinyunFeng, et al."ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving."IET Intelligent Transport Systems 18.10(2024): 1962-1979.

[8]. Shrotriya, Lalit, et al. "Cryptocurrency algorithmic trading with price forecasting analysis using PowerBI." International Journal of Engineering, Science and Technology 15.4 (2023): 1-8.

[9]. Yuwei Hu."Improved YOLOv8 algorithm for vehicle image target detection based on learning rate optimisation strategy".Ed.2024,

Cite this article

Wu,X. (2025). Optimization of YOLOv8 Traffic Sign Object Detection Based on BiFPN Feature Pyramid and CBAM Attention Module. Applied and Computational Engineering,176,70-78.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Machine Learning and Automation

ISBN: 978-1-80590-239-3(Print) / 978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Conference website: 978-1-80590-240-9

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.176

ISSN: 2755-2721(Print) / 2755-273X(Online)

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