Safety Helmet Wear Detection Algorithm Based on ASG-YOLOv8s

In the field of industrial safety, the standardised wearing of safety helmets by workers constitutes a core protective measure against head injuries. However, in industrial settings, multi-scale background interference arising from variations in monitoring distance renders traditional detection models ineffective at capturing the contour features of small-sized helmets. This study, therefore, proposes the ASG-YOLOv8s safety helmet detection network, based on YOLOv8s, to address the challenge of complex scene background interference. First, the AKC-SCAM unit is introduced within the YOLOv8 backbone network to replace certain standard convolutions. This module dynamically adjusts the sampling shape of convolutional kernels, enhancing the extraction of multi-scale defect features. Secondly, a cross-scale interaction architecture (Slim-neck) is constructed in the Neck section, employing GSConv instead of conventional convolutions. This combines with a cross-level feature pyramid to achieve cross-scale interaction between deep semantic features and shallow details. Finally, GAM attention is embedded before the multi-scale output for head detection, establishing a dual-stream attention mechanism that synergistically optimises feature response intensity for low-quality candidate boxes, while suppressing background noise interference. Experimental results demonstrate that the enhanced ASG-YOLOv8s achieves improvements of 2.54%, 2.94%, and 3.16% over the original model in Precision (P), Recall (R), and mean average precision (mAP), respectively, on the SHWD dataset.

@article{He2025,
  title     = {Safety Helmet Wear Detection Algorithm Based on ASG-YOLOv8s},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Li-Zhen He and Zhi-Sheng Wang and Yi-Wei Duan and Jin-Hai Sa},
  doi       = {10.14569/IJACSA.2025.0161244},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161244}
}