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GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

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Abstract

Traffic sign detection is a crucial element of advanced driver assistance systems (ADAS) for environmental perception. However, challenges persist in the detection of small-scale targets and susceptibility to adverse weather, varying light conditions, and occlusions. To address this issue, a novel traffic sign detection algorithm, GRFS-YOLOv8, is proposed. GRFS-YOLOv8 introduces an enhanced greater receptive field-SPPF (GRF-SPPF) module to replace the original SPPF module, enabling the capture of richer multiscale features from image feature maps. Additionally, a new SPAnet architecture is designed by introducing two “shortcut” paths and additional smaller target detection layers to enhance path aggregation capabilities. This architecture propagates complete semantic information, alleviating the reduction in resolution for small targets and enhancing the model’s capability to detect them. Finally, by employing GhostConv and C2fGhost to replace multiple CBS and C2f modules in Backbone and Neck, cost-effective linear operations are utilized to obtain more feature maps, thus reducing the computational cost of the model. Experimental validation across multiple datasets demonstrates the efficacy and adaptability of GRFS-YOLOv8, achieving an 80.3% mAP and 72.4% Recall in CCTSDB 2021, a 71.2% mAP and 95% Recall in TT100K, and a 94.0% mAP and 96.0% Recall in GTSDB, surpassing mainstream detectors and comparative methods.

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Funding

This work is supported by the National Natural Science Foundation of China (62002070), the Science and Technology Plan Project of Guangzhou City (202102021236).

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  1. Guobo Xie and Zhijun Xu have contributed equally to this work.

Authors and Affiliations

  1. School of Computing, Guangdong University of Technology, Guangzhou, 510000, Guangdong, China

    Guobo Xie, Zhijun Xu, Zhiyi Lin & Xingming Liao

  2. School of Mechanical and Electrical Engineering, Hainan University, Haikou, 570000, Hainan, China

    Teng Zhou

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  1. Guobo Xie
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  4. Xingming Liao
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The submission has been received explicitly from all co-authors. And authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.

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Correspondence to Zhiyi Lin.

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Xie, G., Xu, Z., Lin, Z. et al. GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation. SIViP 18, 5519–5534 (2024). https://doi.org/10.1007/s11760-024-03252-8

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