A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
This study presents a hybrid computer vision method combining Mask R-CNN and a color-based algorithm to quantify exposed skin in images for dermal exposure assessment. Using 170 indoor-painting images, the method achieved approximately 80% agreement with human estimates, offering a scalable approach for semi-quantitative exposure analysis. Future work includes body-part recognition, PPE detection, and video-based analysis.
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[Submitted on 28 Jul 2026]
Title:A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
View a PDF of the paper titled A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment, by Hua Qian and 4 other authors
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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.
Comments: 3 pages, 2 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.10; I.4.6; I.5.4
Cite as: arXiv:2607.26170 [cs.CV]
(or arXiv:2607.26170v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26170
arXiv-issued DOI via DataCite (pending registration)
Journal reference: The Synergist, October 2024
Submission history
From: Haining Zheng [view email] [v1] Tue, 28 Jul 2026 18:20:20 UTC (618 KB)
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