A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety
This is an incremental study applying existing object detection methods to a new domain-specific problem of kitchen safety surveillance.
This research tackled the problem of detecting knife safety hazards in kitchens by comparing YOLOv5, YOLOv8, and YOLOv10 models, finding that YOLOv5 performed better in identifying hand-blade contact hazards and YOLOv8 excelled in detecting curled finger hazards.
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.