Open Access

ARTICLE

YOLO-SDW: Traffic Sign Detection Algorithm Based on YOLOv8s Skip Connection and Dynamic Convolution

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

1 School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China
2 Henan Province New Energy Vehicle Power Electronics and Power Transmission Engineering Research Center, Luoyang, 471000, China
3 School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China

* Corresponding Author: Juwei Zhang. Email:

(This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)

Computers, Materials & Continua 2026, 86(1), 1-20. https://doi.org/10.32604/cmc.2025.069053

Received 13 June 2025; Accepted 05 September 2025; Issue published 10 November 2025

Abstract

Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly, a Skip Connection Reconstruction (SCR) module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes. Secondly, a C2f module based on Dynamic Snake Convolution (C2f-DySnake) is proposed to dynamically adjust the receptive field information, improve the algorithm’s feature extraction ability for blurred or occluded targets, and reduce the occurrence of false detections and missed detections. Finally, the Wise Powerful IoU v2 (WPIoUv2) loss function is proposed to further improve the detection accuracy of the algorithm. Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%, and mAP@0.5:0.95 is 68.5%, which is 4% and 3.3% higher than the YOLOv8s baseline, respectively. YOLO-SDW ensures real-time performance while having higher accuracy.

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Keywords

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