{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T03:11:00Z","timestamp":1769569860098,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>Aiming at the challenges of variable fire scale, complex background interference, and high real-time requirements in the fire detection, this paper proposes, this paper proposes LSKNet-YOLO, an improved YOLO model with a Large Selective Kernel Network backbone. By integrating dynamic large kernel convolution and a feature recalibration mechanism, the model enhances multi-scale flame and smoke feature extraction. On a curated fire dataset (1,579 images, 4,656 labeled instances), the improved model achieves an integrated mAP50 of 0.523 (+7.6% over YOLOv11n), with 8.0% and 11.8% improvements in fire and smoke class accuracy, respectively. Despite a moderate inference speed reduction (14.8 ms\/image vs. 0.8 ms\/image), the leakage rate in complex scenarios decreases by 10.2%, significantly enhancing early warning reliability in real-world applications such as industrial facilities and densely populated public spaces. This study provides a reproducible framework for balancing accuracy and real-time performance in fire detection, while explicitly addressing deployment challenges through architectural customization.<\/jats:p>","DOI":"10.3233\/faia251656","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:03Z","timestamp":1769519943000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimization of YOLO Model Based on Backbone Network Replacement: Fire Detection Performance of LSKNet vs. Benchmark Model"],"prefix":"10.3233","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}]},{"given":"Yanan","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}]},{"given":"Yiqin","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}]},{"given":"Chen","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Information Engineering of Nanjing XiaoZhuang University, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251656","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:03Z","timestamp":1769519943000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251656","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}