{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:37:27Z","timestamp":1783438647244,"version":"3.54.6"},"reference-count":74,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071354"],"award-info":[{"award-number":["62071354"]}]},{"name":"National Natural Science Foundation of China","award":["62201419"],"award-info":[{"award-number":["62201419"]}]},{"name":"National Natural Science Foundation of China","award":["2023-YBGY-218"],"award-info":[{"award-number":["2023-YBGY-218"]}]},{"name":"Key Research and Development Program of Shaanxi","award":["62071354"],"award-info":[{"award-number":["62071354"]}]},{"name":"Key Research and Development Program of Shaanxi","award":["62201419"],"award-info":[{"award-number":["62201419"]}]},{"name":"Key Research and Development Program of Shaanxi","award":["2023-YBGY-218"],"award-info":[{"award-number":["2023-YBGY-218"]}]},{"name":"ISN State Key Laboratory","award":["62071354"],"award-info":[{"award-number":["62071354"]}]},{"name":"ISN State Key Laboratory","award":["62201419"],"award-info":[{"award-number":["62201419"]}]},{"name":"ISN State Key Laboratory","award":["2023-YBGY-218"],"award-info":[{"award-number":["2023-YBGY-218"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning (DL)-based object detection algorithms have gained impressive achievements in natural images and have gradually matured in recent years. However, compared with natural images, remote sensing images are faced with severe challenges due to the complex backgrounds and difficult detection of small objects in dense scenes. To address these problems, a novel one-stage object detection model named MDCT is proposed based on a multi-kernel dilated convolution (MDC) block and transformer block. Firstly, a new feature enhancement module, MDC block, is developed in the one-stage object detection model to enhance small objects\u2019 ontology and adjacent spatial features. Secondly, we integrate a transformer block into the neck network of the one-stage object detection model in order to prevent the loss of object information in complex backgrounds and dense scenes. Finally, a depthwise separable convolution is introduced to each MDC block to reduce the computational cost. We conduct experiments on three datasets: DIOR, DOTA, and NWPU VHR-10. Compared with the YOLOv5, our model improves the object detection accuracy by 2.3%, 0.9%, and 2.9% on the DIOR, DOTA, and NWPU VHR-10 datasets, respectively.<\/jats:p>","DOI":"10.3390\/rs15020371","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T04:47:08Z","timestamp":1673239628000},"page":"371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["MDCT: Multi-Kernel Dilated Convolution and Transformer for One-Stage Object Detection of Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Juanjuan","family":"Chen","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hansheng","family":"Hong","sequence":"additional","affiliation":[{"name":"The Guangdong OPPO Mobile Telecommunications Corp., Dongguan 523860, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8096-3370","authenticated-orcid":false,"given":"Bin","family":"Song","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Xu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TIP.2020.3042084","article-title":"Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images","volume":"30","author":"Zhang","year":"2020","journal-title":"IEEE Trans. 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