Knife safety in the kitchen is essential for preventing accidents or injuries with an emphasis on proper handling, maintenance, and storage methods. This research presents a comparative analysis of three YOLO models, YOLOv5, YOLOv8, and YOLOv10, to detect the hazards involved in handling knife, concentrating mainly on ensuring fingers are curled while holding items to be cut and that hands should only be in contact with knife handle avoiding the blade. Precision, recall, F-score, and normalized confusion matrix are used to evaluate the performance of the models. The results indicate that YOLOv5 performed better than the other two models in identifying the hazard of ensuring hands only touch the blade, while YOLOv8 excelled in detecting the hazard of curled fingers while holding items. YOLOv5 and YOLOv8 performed almost identically in recognizing classes such as hand, knife, and vegetable, whereas YOLOv5, YOLOv8, and YOLOv10 accurately identified the cutting board. This paper provides insights into the advantages and shortcomings of these models in real-world settings. Moreover, by detailing the optimization of YOLO architectures for safe knife handling, this study promotes the development of increased accuracy and efficiency in safety surveillance systems.

翻译：厨房刀具安全对于预防事故或伤害至关重要，其重点在于正确的操作、维护与存放方法。本研究对YOLOv5、YOLOv8和YOLOv10三种YOLO模型进行了对比分析，旨在检测刀具操作中的危险行为，主要聚焦于确保切割时手指应保持弯曲握持物品，且手部仅接触刀柄而避免接触刀刃。采用精确率、召回率、F分数及归一化混淆矩阵评估模型性能。结果表明，在识别“手部仅接触刀刃”这一危险行为时，YOLOv5的表现优于另外两种模型；而在检测“握持物品时手指弯曲”这一危险行为时，YOLOv8表现更佳。在手部、刀具、蔬菜等类别的识别上，YOLOv5与YOLOv8表现几乎相同；而YOLOv5、YOLOv8与YOLOv10均能准确识别砧板。本文揭示了这些模型在实际应用中的优势与不足。此外，通过详细阐述针对安全刀具操作的YOLO架构优化方法，本研究有助于推动安全监控系统在准确性与效率方面的提升。