A lightweight network for traffic sign detection via multiple scale context awareness and semantic information guidance

• Published in: Scientific reports Vol. 15; no. 1; pp. 10110 - 16
• Main Authors: Du, Chenjie, Su, Siyu, Lin, Chenwei, Yao, Yingbiao, Jin, Ran, Hong, Xinhua
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 639/705/258; Brain mapping; E-block structure; Humanities and Social Sciences; Lightweight; Multi-scale dilated convolution; multidisciplinary; Receptive field; Science; Science (multidisciplinary); Semantic information guidance; Semantics; Traffic control; Traffic sign detection; Semantic information guidance; E-block structure; Traffic sign detection; Lightweight; Multi-scale dilated convolution
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-94610-0

Traffic sign detection, as a critical branch of object detection, plays an essential role in both assisted driving and autonomous driving technologies. In this paper, we propose MASG-Net, a lightweight detection network designed to improve the accuracy and efficiency of traffic sign detection. First, we introduce a channel attention mechanism into MobileNetV3 to create a novel E-block structure and design E-mobilenet, a lightweight backbone network, to replace the backbone in YOLOv4-tiny, significantly enhancing feature extraction while reducing parameters. Second, we propose a multi-scale dilated convolution spatial pyramid pooling (MDSPP) module to expand the receptive field of feature maps, enabling the network to capture multi-scale contextual information effectively. Finally, a semantic information guidance (SIG) module is introduced to leverage deep semantic information to guide shallow feature layers, improving the detection of small traffic signs and enhancing robustness against cluttered backgrounds. Experimental results on the CCTSDB, GTSDB and TT100K datasets demonstrate that MASG-Net achieves superior detection performance, particularly for small and challenging traffic signs, while maintaining high efficiency with an inference speed of 203.6 FPS. These results highlight MASG-Net’s potential for real-time traffic sign detection in practical applications.