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China","award":["GXKL06200214"],"award-info":[{"award-number":["GXKL06200214"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["GXKL06200205"],"award-info":[{"award-number":["GXKL06200205"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["cqupt-mct-202103"],"award-info":[{"award-number":["cqupt-mct-202103"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["cstc2021jcyj-bshX0085"],"award-info":[{"award-number":["cstc2021jcyj-bshX0085"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.<\/jats:p>","DOI":"10.3390\/rs14153829","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3829","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiuqin","family":"Li","sequence":"first","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Hongqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Jun","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2590-7704","authenticated-orcid":false,"given":"Zhanye","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Qinghua","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1109\/TGRS.2016.2514494","article-title":"Ground-moving target imaging and velocity estimation based on mismatched compression for bistatic forward-looking SAR","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. 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