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Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the mAP@0.5 of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds\u2019 monitoring and ecological environment management.<\/jats:p>","DOI":"10.3390\/rs15071796","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T04:37:15Z","timestamp":1679978235000},"page":"1796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An Improved YOLOv5 Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhenhui","family":"Sun","sequence":"first","affiliation":[{"name":"School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China"},{"name":"Tianjin Key Laboratory of Soft Soil Characteristics and Engineering Environment, Tianjin University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peihang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5440-4081","authenticated-orcid":false,"given":"Qingyan","family":"Meng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxiao","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0979-4084","authenticated-orcid":false,"given":"Yaxin","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, York Street, Belfast BT15 1ED, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Che, D., Liang, A., Li, X., and Ma, B. 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