{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:56:49Z","timestamp":1781110609245,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foreign Scholars in University Research and Teaching Programs (the 111 Project)","award":["B18039"],"award-info":[{"award-number":["B18039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data augmentation is a crucial technique for convolutional neural network (CNN)-based object detection. Thus, this work proposes BoxPaste, a simple but powerful data augmentation method appropriate for ship detection in Synthetic Aperture Radar (SAR) imagery. BoxPaste crops ship objects from one SAR image using bounding box annotations and pastes them on another SAR image to artificially increase the object density in each training image. Furthermore, we dive deep into the characteristics of the SAR ship detection task and draw a principle for designing a SAR ship detector\u2014light models may perform better. Our proposed data augmentation method and modified ship detector attain a 95.5% Average Precision (AP) and 96.6% recall on the SAR Ship Detection Dataset (SSDD), 4.7% and 5.5% higher than the fully convolutional one-stage (FCOS) object detection baseline method. Furthermore, we also combine our data augmentation scheme with two current detectors, RetinaNet and adaptive training sample selection (ATSS), to validate its effectiveness. The experimental results demonstrate that our newly proposed SAR-ATSS architecture achieves 96.3% AP, employing ResNet-50 as the backbone. The experimental results show that the method can significantly improve detection performance.<\/jats:p>","DOI":"10.3390\/rs14225761","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["BoxPaste: An Effective Data Augmentation Method for SAR Ship Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhiling","family":"Suo","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6453-0786","authenticated-orcid":false,"given":"Yongbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3519-1137","authenticated-orcid":false,"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yili","family":"Hu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/0031-3203(90)90101-P","article-title":"Multi-frame ship detection and tracking in an infrared image sequence","volume":"23","author":"Lee","year":"1990","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mingbo, Z., Jianwu, Z., and Jianguo, H. 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