一张图片胜过千言万语——利用混合深度学习从图像中提取皮肤暴露数据以增强安全性评估
A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
AI总结：
该研究开发混合计算机视觉方法，用Mask R-CNN和颜色算法处理170张室内绘画图像，量化皮肤暴露，与人工估计一致性约80%，可扩展至多场景安全评估。
AI中文摘要：
本研究开发了一种混合计算机视觉方法，用于从图像中量化暴露的皮肤，以开展皮肤暴露评估。使用170张室内绘画图像，Mask R-CNN首先识别人体对象并去除背景干扰；随后采用基于颜色的算法分割暴露的皮肤。得到的暴露皮肤与身体的像素比例与人工估计的一致性约为80%。该方法展示了一种从图像中提取半定量暴露信息的可扩展方式，未来可扩展至身体部位识别、PPE检测以及基于视频的暴露分析。
英文摘要：
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.
