{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:52:34Z","timestamp":1762609954980,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Program of the State Administration for Market Regulation of China","award":["2021MK90"],"award-info":[{"award-number":["2021MK90"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning-based object detection has become a powerful tool in dress code monitoring. However, even state-of-the-art detection models inevitably suffer from false alarms or missed detections, especially when handling small targets such as hats and masks. To overcome these limitations, this paper proposes a novel method for dress code monitoring using an improved YOLOv8n model, the DeepSORT tracking, and a new dress code judgment criterion. We improve the YOLOv8n model through three means: (1) a new neck structure named FPN-PAN-FPN (FPF) is introduced to enhance the model\u2019s feature fusion capability, (2) Receptive-Field Attention convolutional operation (RFAConv) is utilized to better capture the difference in information brought by different positions, and a (3) Focused Linear Attention (FLatten) mechanism is added to expand the model\u2019s receptive field. This improved YOLOv8n model increases mAP while reducing model size. Next, DeepSORT is integrated to obtain instance information across multi-frames. Finally, we adopt a new judgment criterion to conduct real-scene dress code monitoring. The experimental results show that our method effectively identifies instances of dress violations, reduces false alarms, and improves accuracy.<\/jats:p>","DOI":"10.3390\/s24186063","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T09:23:04Z","timestamp":1726737784000},"page":"6063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dress Code Monitoring Method in Industrial Scene Based on Improved YOLOv8n and DeepSORT"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7336-7238","authenticated-orcid":false,"given":"Jiadong","family":"Zou","sequence":"first","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-9386","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5122-3472","authenticated-orcid":false,"given":"Songxiao","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}]},{"given":"Qing","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Na, Z., Zechuan, Y., You, H., Xiaoan, B., and Yifan, S. 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