A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety

TL;DR

This paper compares YOLOv5, YOLOv8, and YOLOv10 models for detecting kitchen knife hazards, highlighting their strengths and weaknesses in safety applications.

Contribution

It provides a detailed comparative analysis of three YOLO models optimized for kitchen safety hazard detection, emphasizing their performance differences.

Findings

YOLOv5 outperformed others in detecting hand-blade contact hazards.

YOLOv8 was best at identifying curled fingers during handling.

All models accurately recognized the cutting board.

Abstract

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.