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基准测试轻量级YOLO物体探测器用于实时卫生合规监测

Leen Alashrafi¹, Raghad Badawood¹, Hana Almagrabi¹

¹Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|October 16, 2025

概括

本研究对用于自动化个人防护设备 (PPE) 合规监测的轻量级物体检测模型进行了基准测试. 在受监管环境中,YOLOv10n展示了准确性和效率在实时卫生执法的最佳平衡.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
公共卫生公共卫生

背景情况:

在受监管的环境 (食品加工,医院) 中,遵守卫生要求需要可靠的个人防护设备 (PPE) 检测.
对个人防护设备 (手套,口罩,发网) 的使用进行手动检查对于持续的实时监控是无效的.

研究的目的:

以轻量级物体检测模型 (YOLOv8n,YOLOv10n,YOLOv12n) 为自动化个人防护设备合规性监测进行基准测试.
为了评估模型性能,使用准确性和效率指标进行实际部署.

主要方法:

使用了超过31,000张注释图像的精心策划的数据集,涵盖了七个PPE合规类别.
评估了三个纳米级YOLO模型的检测准确性 (mAP,精度,回忆) 和效率 (推断速度,模型大小,GFLOPs).

主要成果:

YOLOv10n获得了最高的mAP@50 (85.7%),具有具有竞争力的效率,适合物联网部署.
在更严格的值 (mAP@50-95) 上,YOLOv8n提供了更高的定位精度.
YOLOv12n优先考虑了超轻型操作,牺牲了一些准确性.

结论:

由于其平衡的性能,YOLOv10n是实时PPE合规系统的强有力的候选者.
该研究提供了一个可重复的,部署意识的框架,用于在卫生关键设置中的计算机视觉.
结果指导了纳米级检测模型的选择,以加强卫生监测.

关键词:

物联网集成系统与物联网集成的系统.基准测试 (benchmarking) 是一种比较的方法.计算机视觉计算机视觉深度学习是一种深度学习.符合卫生标准的遵守.模型的效率效率模型.对象检测检测对象检测对象检测个人防护设备 (PPE)实时监控实时监控

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