Performance of YOLOv7 in Kitchen Safety While Handling Knife
This addresses kitchen safety for individuals handling knives, but it is incremental as it applies an existing method to a new domain.
The study tackled the problem of detecting safety risks during knife handling in kitchens, such as improper finger placement and blade contact, using the YOLOv7 object detection model, and achieved a mAP50-95 score of 0.7879, precision of 0.9063, and recall of 0.7503.
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.