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On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.<\/jats:p>","DOI":"10.1007\/s40747-024-01444-w","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T07:01:30Z","timestamp":1716015690000},"page":"5703-5720","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DATFNets-dynamic adaptive assigned transformer network for fire detection"],"prefix":"10.1007","volume":"10","author":[{"given":"Zuoxin","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6796-175X","authenticated-orcid":false,"given":"Xiaohu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Dunqing","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"issue":"6","key":"1444_CR1","first-page":"947","volume":"17","author":"A Khondaker","year":"2020","unstructured":"Khondaker A, Khandaker A, Uddin J (2020) Computer vision-based early fire detection using enhanced chromatic segmentation and optical flow analysis technique. 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