{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:27:19Z","timestamp":1791232039967,"version":"4.1.0"},"reference-count":25,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Province Science and Technology Program","award":["BX2023"],"award-info":[{"award-number":["BX2023"]}]},{"name":"China Civil Engineering Society Research Project","award":["CCES2024KT10"],"award-info":[{"award-number":["CCES2024KT10"]}]},{"name":"China State Construction Engineering Corporation Technology R&D Program","award":["CSCEC-2024-Q-74"],"award-info":[{"award-number":["CSCEC-2024-Q-74"]}]},{"name":"Chongqing Construction Science and Technology Program","award":["CQCS2024-8-5"],"award-info":[{"award-number":["CQCS2024-8-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Construction sites are complex environments, and unsafe behaviors by workers, such as not wearing safety helmets or reflective vests, can easily lead to accidents. When using target detection technology to detect unsafe behaviors, the results are often unsatisfactory due to the complexity of the background and the small size of the targets. This paper proposes an unsafe behavior detection algorithm based on dual adaptive feature fusion. The algorithm is based on YOLOv5, introducing a front-end adaptive feature fusion module (FE-AFFM) at the head of the backbone network for deep data processing, improving the model\u2019s feature extraction capability in complex backgrounds. Simultaneously, a back-end adaptive feature fusion module (BE-AFFM) is introduced at the tail of the network to strengthen feature fusion. In the experimental verification phase, this paper selects a self-made laboratory dataset and verifies the effectiveness of the improved algorithm through ablation experiments, algorithm comparisons, and heatmap analysis. The average accuracy of the improved algorithm is 3.6% higher than the baseline model, and the detection effect on small targets is significantly improved, meeting the actual needs of construction sites. This paper also selects the publicly available dataset SHWD for algorithm comparison experiments. The results show that the improved algorithm still has a significant advantage over mainstream algorithms, verifying the generalization ability of the improved model.<\/jats:p>","DOI":"10.3390\/s25237216","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T11:43:22Z","timestamp":1764157402000},"page":"7216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DAF-YOLO: Detection of Unsafe Behaviors on Construction Sites"],"prefix":"10.3390","volume":"25","author":[{"given":"Qi","family":"Xu","sequence":"first","affiliation":[{"name":"College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejun","family":"Jia","sequence":"additional","affiliation":[{"name":"China Construction Second Engineering Bureau Co., Ltd., Beijing 100160, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiao","family":"Wang","sequence":"additional","affiliation":[{"name":"China Construction Second Engineering Bureau Co., Ltd., Beijing 100160, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9566-7865","authenticated-orcid":false,"given":"Guangming","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","unstructured":"National Bureau of Statistics (2025, October 18). 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