YOLO-DPDG: A Dual-Pooling Dynamic Grouping Network for Small and Long-Distance Traffic Sign Detection
Source abstract
Abstract
Traffic sign detection is a crucial task for autonomous driving perception systems, as it directly impacts vehicle path planning and safety decisions. Existing algorithms face challenges such as feature information attenuation and model lightweighting requirements in the detection of small traffic signs at long distances. To address these issues, this paper proposes a dual-pooling dynamic grouping (DPDG) module. This module dynamically adjusts the number of groups to adapt to different input features, combines global average pooling and max pooling to enhance channel attention representation, and uses a lightweight 3 × 3 convolution-based spatial branch to generate spatial weights. Based on a hierarchical optimization strategy, the DPDG module is integrated into the YOLOv10n network. Experimental results on the traffic sign dataset demonstrate a significant improvement in the performance of the YOLO-DPDG network: Compared to the baseline YOLOv10n model, mAP@0.5 and mAP@0.5:0.95 improved by 8.77% and 10.56%, respectively, while precision and recall were enhanced by 6.16% and 6.62%, respectively. Additionally, inference speed (FPS) increased by 11.1%, with only a 4.89% increase in model parameters. Compared to the YOLOv10-Small model, this method achieves a similar detection accuracy while reducing the number of model parameters by 64.83%. This study provides a more efficient and lightweight solution for edge-based traffic sign detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Infrastructure Maintenance and Monitoring · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Funding
Major Science and Technology Foundation of Zhongshan City · First-Class Course Program of Guangdong Province · Science and Technology Commissioner Project of Guangdong Province · Guangdong Basic and Applied Basic Research Foundation
References (40)
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