  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.
