用图像分析皮肤暴露量，提升职业安全评估效率
A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
- 结合掩膜R-CNN与颜色算法，自动分割图像中暴露的皮肤区域
- 皮肤暴露像素比与人工评估一致率达80%以上
- 适合需要快速评估工作场景皮肤暴露的研究者和安全工程师
本研究提出一种混合深度学习方法，通过图像量化皮肤暴露程度，用于改善接触评估。基于170张室内绘画场景图像，首先使用Mask R-CNN识别人体并去除背景干扰；随后采用基于颜色的算法分割暴露皮肤区域。结果表明，计算所得的暴露皮肤像素占比与人工估算结果约有80%的一致性。该方法为从图像中提取半定量暴露信息提供了可扩展的方案，未来可拓展至身体部位识别、个人防护装备检测及视频动态暴露分析。
原文摘要 · Abstract (English)
This study developed a hybrid computer vision method to quantify exposed skin from images for dermal exposure assessment. Using 170 indoor-painting images, Mask R-CNN first identified human subjects and removed background interference; a color-based algorithm then segmented exposed skin. The resulting exposed-skin-to-body pixel ratios showed approximately 80% agreement with human estimates. The approach demonstrates a scalable way to extract semi-quantitative exposure information from images, with future extensions to body-part recognition, PPE detection, and video-based exposure analysis.
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