{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:35:59Z","timestamp":1783697759452,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Province Industry-University-Research Cooperation Project","award":["BY20230913"],"award-info":[{"award-number":["BY20230913"]}]},{"name":"Department of Science and Technology of Jiangsu Province"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to solve the challenges of laboratory safety management in universities, such as insufficient supervision of high-frequency risk behaviors in responsibility traceability, a laboratory safety hazard perception and responsibility traceability system based on deep learning is proposed. Based on the YOLOv5s object detection model, the channel attention mechanism SE and NWD loss functions are introduced, with DeepSORT tracking to realize multi-target tracking and hidden danger perception in laboratory scenarios. Then, the responsibility matching algorithm and visual traceability mechanism are proposed to build a full-chain management system of \u201crisk perception, analysis and tracking, and responsibility traceability\u201d. Experiments show that the mean average precision (mAP) of YOLO-lab in the laboratory scene is 87.8%. Taking the experimenter not wearing a lab coat as an example, through the test of the laboratory scene, the multi-target tracking effect is excellent and the responsibility traceability report is generated, which solves the problem of \u201cvisible and uncontrollable behavior, traceable and unproven\u201d in traditional supervision, and provides an intelligent technical path for laboratory safety governance.<\/jats:p>","DOI":"10.3390\/s26030953","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T12:46:11Z","timestamp":1770122771000},"page":"953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research and Application of Safety Hazard Perception and Responsibility Traceability System in University Laboratories"],"prefix":"10.3390","volume":"26","author":[{"given":"Rundong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105054","DOI":"10.1016\/j.jlp.2023.105054","article-title":"Risk assessment of safety level in university laboratories using questionnaire and Bayesian network","volume":"83","author":"Zhao","year":"2023","journal-title":"J. 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