Performance of YOLOv7 in Kitchen Safety While Handling Knife

TL;DR

This study evaluates YOLOv7's effectiveness in detecting kitchen safety hazards related to knife handling, demonstrating its high accuracy and potential to improve safety practices.

Contribution

The paper presents a novel application of YOLOv7 for identifying safety risks during knife handling in kitchens, with performance metrics indicating high detection accuracy.

Findings

YOLOv7 achieved a mAP50-95 score of 0.7879 at epoch 31.

Precision of 0.9063 and recall of 0.7503 demonstrate high detection performance.

The model effectively identifies improper finger placement and blade contact hazards.

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.