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Our approach comprises a multiscale feature extractor (MSFE) module, which employs diverse convolutional kernels to reduce model complexity while capturing multiscale features effectively. Additionally, we devise a dynamic scale sequence fusion (DSSF) module, which enables comprehensive exploration and efficient integration of multiscale features across different levels. The proposed approach is evaluated across three publicly accessible datasets: VisDrone2019, UVADT and DIOR. The results demonstrate that our approach achieves lightweight models while maintaining high detection accuracy. <\/jats:p>","DOI":"10.1142\/s0218126625502986","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T04:56:27Z","timestamp":1742619387000},"source":"Crossref","is-referenced-by-count":1,"title":["DTSFNet: A Lightweight Network Based on Dynamic Sampling and Scale Sequence Fusion for Aerial Image Object Detection"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9643-1394","authenticated-orcid":false,"given":"Yanting","family":"Liao","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 401331, China and Heavy Rainfall Research Center of China, No. 3, Donghu East Road, Hongshan District, Wuhan 430074, 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