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Research on Traffic Sign Recognition Based on the Improved YOLOv5s-CBAM-ASFF Algorithm
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Rongping Fu, Jiansheng Fu, Wangyang Liang
Automotive Engineer | 2025, (8) : 22 - 28
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Automotive Engineer | 2025, (8): 22-28
Research on Traffic Sign Recognition Based on the Improved YOLOv5s-CBAM-ASFF Algorithm
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Rongping Fu, Jiansheng Fu, Wangyang Liang
Affiliations
  • Guilin University of Electronic Technology, Guilin 541000
Published: 2025-08-15 doi: 10.20104/j.cnki.1674-6546.20240263
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To achieve more efficient detection of small traffic sign targets under complex urban street background conditions, this paper proposes an improved YOLOv5s algorithm. This enhancement is achieved by incorporating a Convolution Block Attention Module (CBAM) Spatial Channel Attention Mechanism, an Adaptive Spatial Feature Fusion (ASFF) module, and an improved loss function for detection boxes. The validation results on the TT100K traffic sign dataset demonstrate that the proposed algorithm achieves a mean Average Precision (mAP) of 84.5% in traffic sign recognition.

Deep learning  /  Traffic sign recognition  /  Attention mechanism  /  Multi-scale feature fusion  /  YOLOv5s
Rongping Fu, Jiansheng Fu, Wangyang Liang. Research on Traffic Sign Recognition Based on the Improved YOLOv5s-CBAM-ASFF Algorithm[J]. Automotive Engineer, 2025 , (8) : 22 -28 . DOI: 10.20104/j.cnki.1674-6546.20240263
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doi: 10.20104/j.cnki.1674-6546.20240263
  • Online Date：2025-10-29
  • Published：2025-08-15
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  • Revised：2024-09-08
Affiliations
    Guilin University of Electronic Technology, Guilin 541000
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