YOLOv7可精准识别厨房切菜时的刀具安全隐患
Performance of YOLOv7 in Kitchen Safety While Handling Knife
- 用YOLOv7检测切菜时手指位置与刀刃接触
- 最佳表现时mAP50-95达0.7879，精度0.9063，召回率0.7503
- 适合智能厨房安全系统研发者参考
厨房中安全使用刀具能显著降低切割伤、受伤及严重事故风险。本研究采用先进目标检测模型YOLOv7，聚焦于识别刀具操作中的安全隐患，尤其是手指不当放置和刀刃接触手部的情况。通过精确率、召回率、mAP50和mAP50-95等指标评估模型性能。结果表明，YOLOv7在第31轮训练时达到最优表现，mAP50-95为0.7879，精确率为0.9063，召回率为0.7503。这些发现凸显了YOLOv7在准确检测刀具相关危险方面的潜力，有助于推动厨房安全技术的发展。
原文摘要 · Abstract (English)
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.
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