{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:05:51Z","timestamp":1755223551962,"version":"3.43.0"},"reference-count":46,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p> The correct use of personal protective equipment (PPE) is crucial for mitigating occupational hazards and ensuring the safety of workers. The standards for PPE usage can vary depending on the nature of the job and the individual\u2019s role. Even within the same role, there may be multiple correct ways to wear PPE. The conventional approach to PPE compliance detection typically treats it as a classification problem, which often requires the reconstruction of the dataset and the retraining of the model, leading to limited scalability. This paper introduces a method that merges object detection and pose estimation techniques. This method is structured around three key components including a worker detection module, a part segmentation module, and a template matching module. By acquiring a small set of standard images and making adjustments to the standard template library, the method is able to adapt to various PPE requirements. Experimental results in two phases with two types of PPE indicate high accuracy in detecting worker PPE compliance. The method\u2019s ability to swiftly and accurately identify compliance makes it well suited for real-world applications, and its capability to adjust to different PPE standards with minimal additional workload highlights its robust scalability. <\/jats:p>","DOI":"10.1142\/s1793962325500448","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T04:22:02Z","timestamp":1747801322000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep learning for industrial safety: A flexible template-based approach for personal protective equipment compliance"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9999-7128","authenticated-orcid":false,"given":"Yimeng","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing 100084, P. R. 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