Abstract
Safe knife practices in the kitchen significantly reduce the risk of cuts, injuries, and serious accidents during food preparation. Using YOLOv7, an advanced object detection model, this study focuses on identifying safety risks during knife handling, particularly improper finger placement and blade contact with hand. The model's performance was evaluated using metrics such as precision, recall, mAP50, and mAP50-95. The results demonstrate that YOLOv7 achieved its best performance at epoch 31, with a mAP50-95 score of 0.7879, precision of 0.9063, and recall of 0.7503. These findings highlight YOLOv7's potential to accurately detect knife-related hazards, promoting the development of improved kitchen safety.
Abstract (translated)
厨房中安全使用刀具可以显著降低在食品准备过程中割伤、受伤和严重事故的风险。本研究利用YOLOv7，一种先进的物体检测模型，专注于识别刀具操作过程中的安全隐患，特别是不当的手指放置和刀刃与手的接触情况。通过精确度（precision）、召回率（recall）、mAP50以及mAP50-95等指标评估了该模型的表现。结果表明，YOLOv7在第31个周期时表现最佳，其mAP50-95得分为0.7879，准确率为0.9063，召回率为0.7503。这些发现突显了YOLOv7能够精确检测刀具相关隐患的潜力，并促进了厨房安全改进的发展。
URL
https://arxiv.org/abs/2501.05399
