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arXiv · 2607.26170

A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment

Hua Qian·Manisha Kotha·Tuan Tran·Jennifer Shin·Haining Zheng
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Abstract

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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Hua Qian, Manisha Kotha, Tuan Tran, Jennifer Shin, Haining Zheng. 2026-07-28. A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment. https://arxiv.org/abs/2607.26170

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