{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:28:45Z","timestamp":1775665725730,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The symmetry between production efficiency and safety is a crucial aspect of industrial operations. To enhance the identification of proper safety harness use by workers at height, this study introduces a machine vision approach as a substitute for manual supervision. By focusing on the safety rope that connects the worker to an anchor point, we propose a semantic segmentation mask annotation principle to evaluate proper harness use. We introduce CEMFormer, a novel semantic segmentation model utilizing ConvNeXt as the backbone, which surpasses the traditional ResNet in accuracy. Efficient Multi-Scale Attention (EMA) is incorporated to optimize channel weights and integrate spatial information. Mask2Former serves as the segmentation head, enhanced by Poly Loss for classification and Log-Cosh Dice Loss for mask loss, thereby improving training efficiency. Experimental results indicate that CEMFormer achieves a mean accuracy of 92.31%, surpassing the baseline and five state-of-the-art models. Ablation studies underscore the contribution of each component to the model\u2019s accuracy, demonstrating the effectiveness of the proposed approach in ensuring worker safety.<\/jats:p>","DOI":"10.3390\/sym16111449","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T04:59:54Z","timestamp":1730437194000},"page":"1449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Safety in High-Altitude Work: Semantic Segmentation of Safety Harnesses with CEMFormer"],"prefix":"10.3390","volume":"16","author":[{"given":"Qirui","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1797-4936","authenticated-orcid":false,"given":"Dandan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.11622\/smedj.2022017","article-title":"Characteristics of injuries resulting from falls from height in the construction industry","volume":"64","author":"Anantharaman","year":"2023","journal-title":"Singap. 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