A feature‐enhanced hybrid attention network for traffic sign recognition in real scenes

Autor: Lewei He, Fucai Lan, Chuanzhe Zhou, Yaoguang Ye, Wencong Zhang, Bingzhi Chen, Jiahui Pan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Image Processing, Vol 18, Iss 8, Pp 2064-2077 (2024)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
26473046
DOI: 10.1049/ipr2.13083
Popis: Abstract Currently, traffic sign recognition techniques have been brought into the assistive driving of automobiles. However, small traffic sign recognition in real scenes is still a challenging task due to the class imbalance issue and the size limit of the traffic signs. To address the above issues, a feature‐enhanced hybrid attention network is proposed based on YOLOv5s for a small, fast, and accurate traffic sign detector. First, a series of online data augmentation strategies are designed in the preprocessing module for the model training. Second, the hybrid channel and spatial attention module CSAM are integrated into the backbone for a better feature extraction ability. Third, the channel attention module CAM is used in the detection head for a more efficient feature fusion ability. To validate the approach, extensive experiments are conducted based on the Tsinghua‐Tencent 100K dataset. It is found that the novel method achieves state‐of‐the‐art performance with only negligible increases in the model parameter and computational overhead. Specifically, the mAP@0.5, parameters, and FLOPs are 85.8%, 7.13 M, and 16.1 G, respectively.
Databáze: Directory of Open Access Journals
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