Jiuli Campus, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel). 2023 Dec 18;23(24):9906. doi: 10.3390/s23249906.
Wearing gloves during machinery operation in workshops is essential for preventing accidental injuries, such as mechanical damage and burns. Ensuring that workers are wearing gloves is a key strategy for accident prevention. Consequently, this study proposes a glove detection algorithm called YOLOv8-AFPN-M-C2f based on YOLOv8, offering swifter detection speeds, lower computational demands, and enhanced accuracy for workshop scenarios. This research innovates by substituting the head of YOLOv8 with the AFPN-M-C2f network, amplifying the pathways for feature vector propagation, and mitigating semantic discrepancies between non-adjacent feature layers. Additionally, the introduction of a superficial feature layer enriches surface feature information, augmenting the model's sensitivity to smaller objects. To assess the performance of the YOLOv8-AFPN-M-C2f model, this study conducted multiple experiments using a factory glove detection dataset compiled for this study. The results indicate that the enhanced YOLOv8 model surpasses other network models. Compared to the baseline YOLOv8 model, the refined version shows a 2.6% increase in mAP@50%, a 63.8% rise in FPS, and a 13% reduction in the number of parameters. This research contributes an effective solution for the detection of glove adherence.
在车间机械操作时戴手套对于防止意外伤害（如机械损伤和烧伤）至关重要。确保工人戴手套是预防事故的关键策略。因此，本研究提出了一种名为 YOLOv8-AFPN-M-C2f 的手套检测算法，该算法基于 YOLOv8，在车间场景下具有更快的检测速度、更低的计算需求和更高的准确性。本研究的创新之处在于用 AFPN-M-C2f 网络替换了 YOLOv8 的头部，放大了特征向量传播的路径，减轻了非相邻特征层之间的语义差异。此外，引入浅层特征层丰富了表面特征信息，提高了模型对较小物体的敏感性。为了评估 YOLOv8-AFPN-M-C2f 模型的性能，本研究使用为这项研究专门编制的工厂手套检测数据集进行了多次实验。结果表明，增强后的 YOLOv8 模型优于其他网络模型。与基线 YOLOv8 模型相比，改进后的版本在 mAP@50% 上提高了 2.6%，在 FPS 上提高了 63.8%，在参数数量上减少了 13%。本研究为手套贴合度检测提供了一种有效的解决方案。
