{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:22:45Z","timestamp":1778692965071,"version":"3.51.4"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To overcome these issues, this paper introduces a novel smoking detection algorithm, YOLOv8-MNC, which builds on the YOLOv8 network and includes a specialized layer for small target detection. The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy; (2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network\u2019s global feature learning capacity; and (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. The YOLOv8-MNC model achieved a detection accuracy of 85.887%, signifying a remarkable increase of 5.7% in the mean Average Precision (mAP@0.5) when compared to the previous algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The YOLOv8-MNC algorithm represents a valuable step forward in resolving existing problems in smoking behavior detection. Its enhanced performance in both detection accuracy and robustness indicates potential applicability in related fields, thus illustrating a meaningful advancement in the sphere of smoking behavior detection. Future efforts will focus on refining this technique and exploring its application in broader contexts.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fncom.2023.1243779","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T09:26:08Z","timestamp":1692869168000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Smoking behavior detection algorithm based on YOLOv8-MNC"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanfang","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peibei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/SKIMA47702.2019.8982427","article-title":"Deep learning with convolutional neural network and long short-term memory for phishing detection","author":"Adebowale","year":"2019","journal-title":"Proceeding of the 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2019)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2202.06934","article-title":"Slicing aided hyper inference and fine-tuning for small object detection.","author":"Akyon","year":"2022","journal-title":"arXiv"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1007\/s13534-020-00147-8","article-title":"mPuff: Automated detection of cigarette smoking puffs from respiration measurements","author":"Ali","year":"2012","journal-title":"Proceeding of the 2012 ACM\/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1093\/ntr\/ntaa155","article-title":"The United States National Cancer Institute\u2019s coordinated research effort on tobacco use as a major cause of morbidity and mortality among people with HIV.","volume":"23","author":"Ashare","year":"2021","journal-title":"Nicotine Tob. 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