{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:46:55Z","timestamp":1776887215954,"version":"3.51.2"},"reference-count":63,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key project of the National Nature Science Foundation of China","award":["61932012"],"award-info":[{"award-number":["61932012"]}]},{"name":"Key project of the National Nature Science Foundation of China","award":["202101090301013"],"award-info":[{"award-number":["202101090301013"]}]},{"name":"Key project of the National Nature Science Foundation of China","award":["KM202111417007"],"award-info":[{"award-number":["KM202111417007"]}]},{"name":"Key project of the National Nature Science Foundation of China","award":["ZK80202003"],"award-info":[{"award-number":["ZK80202003"]}]},{"name":"Science and Technology Major Project of Shanxi Province, China","award":["61932012"],"award-info":[{"award-number":["61932012"]}]},{"name":"Science and Technology Major Project of Shanxi Province, China","award":["202101090301013"],"award-info":[{"award-number":["202101090301013"]}]},{"name":"Science and Technology Major Project of Shanxi Province, China","award":["KM202111417007"],"award-info":[{"award-number":["KM202111417007"]}]},{"name":"Science and Technology Major Project of Shanxi Province, China","award":["ZK80202003"],"award-info":[{"award-number":["ZK80202003"]}]},{"name":"Beijing Municipal Education Commission Science and Technology Program","award":["61932012"],"award-info":[{"award-number":["61932012"]}]},{"name":"Beijing Municipal Education Commission Science and Technology Program","award":["202101090301013"],"award-info":[{"award-number":["202101090301013"]}]},{"name":"Beijing Municipal Education Commission Science and Technology Program","award":["KM202111417007"],"award-info":[{"award-number":["KM202111417007"]}]},{"name":"Beijing Municipal Education Commission Science and Technology Program","award":["ZK80202003"],"award-info":[{"award-number":["ZK80202003"]}]},{"name":"Academic Research Projects of Beijing Union University","award":["61932012"],"award-info":[{"award-number":["61932012"]}]},{"name":"Academic Research Projects of Beijing Union University","award":["202101090301013"],"award-info":[{"award-number":["202101090301013"]}]},{"name":"Academic Research Projects of Beijing Union University","award":["KM202111417007"],"award-info":[{"award-number":["KM202111417007"]}]},{"name":"Academic Research Projects of Beijing Union University","award":["ZK80202003"],"award-info":[{"award-number":["ZK80202003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for improving performance on limited data. However, we empirically find that due to the dataset discrepancy, directly transferring the model trained on a general object dataset to small-object datasets obtains inferior performance. In this paper, we propose TranSDet, a novel approach for effective transfer learning for small-object detection. Our method adapts a model trained on a general dataset to a small-object-friendly model by augmenting the training images with diverse smaller resolutions. A dynamic resolution adaptation scheme is employed to ensure consistent performance on various sizes of objects using meta-learning. Additionally, the proposed method introduces two network components, an FPN with shifted feature aggregation and an anchor relation module, which are compatible with transfer learning and effectively improve small-object detection performance. Extensive experiments on the TT100K, BUUISE-MO-Lite, and COCO datasets demonstrate that TranSDet achieves significant improvements compared to existing methods. For example, on the TT100K dataset, TranSDet outperforms the state-of-the-art method by 8.0% in terms of the mean average precision (mAP) for small-object detection. On the BUUISE-MO-Lite dataset, TranSDet improves the detection accuracy of RetinaNet and YOLOv3 by 32.2% and 12.8%, respectively.<\/jats:p>","DOI":"10.3390\/rs15143525","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T01:52:25Z","timestamp":1689213145000},"page":"3525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["TranSDet: Toward Effective Transfer Learning for Small-Object Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-6273","authenticated-orcid":false,"given":"Xinkai","family":"Xu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0251-117X","authenticated-orcid":false,"given":"Hailan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-8322","authenticated-orcid":false,"given":"Yan","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8413-123X","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Bao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"Volume 39","author":"Ren","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. 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