Abstract
Context-aware safety monitoring based on computer vision has received relatively little attention, although it is critical for recognizing the working context of workers and performing precise safety assessment with respect to Personal Protective Equipment (PPE) compliance checks. To address this knowledge gap, this study proposes vision-based monitoring approaches for context-aware PPE compliance checks using a modularized analysis pipeline composed of object detection, semantic segmentation, and depth estimation. The efficacy of two different approaches under this methodology was examined using YUD-COSAv2 data collected from actual construction sites. In experiments, the proposed method was able to distinguish between workers at heights and on the ground, applying different PPE compliance rules for evaluating workers’ safety. The depth estimation model achieved an Average Precision of 78.50%, while the segmentation model achieved an Average Precision of 86.22%.
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Acknowledgements
This research was conducted with the support of the “2022 Yonsei University Future-Leading Research Initiative (No. 2022-22-0102)” and the “National R&D Project for Smart Construction Technology (No. 22SMIP-A158708-03)” funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.
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Chern, WC., Hyeon, J., Nguyen, T.V., Asari, V.K., Kim, H. (2024). Context-Aware PPE Compliance Check in Far-Field Monitoring. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_15
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DOI: https://doi.org/10.1007/978-3-031-35399-4_15
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