{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:53:32Z","timestamp":1760151212356,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vehicle detection in remote sensing imagery is a challenging task because of its inherent attributes, e.g., dense parking, small sizes, various angles, etc. Prevalent vehicle detectors adopt an oriented\/rotated bounding box as a basic representation, which needs to apply a distance regression of height, width, and angles of objects. These distance-regression-based detectors suffer from two challenges: (1) the periodicity of the angle causes a discontinuity of regression values, and (2) small regression deviations may also cause objects to be missed. To this end, in this paper, we propose a new vehicle modeling strategy, i.e., regarding each vehicle-rotated bounding box as a saliency area. Based on the new representation, we propose SR-Net (saliency region representation network), which transforms the vehicle detection task into a saliency object detection task. The proposed SR-Net, running in a distance (e.g., height, width, and angle)-regression-free way, can generate more accurate detection results. Experiments show that SR-Net outperforms prevalent detectors on multiple benchmark datasets. Specifically, our model yields 52.30%, 62.44%, 68.25%, and 55.81% in terms of AP on DOTA, UCAS-AOD, DLR 3K Munich, and VEDAI, respectively.<\/jats:p>","DOI":"10.3390\/rs14061313","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T02:10:35Z","timestamp":1646878235000},"page":"1313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Fanfan","family":"Liu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5553","DOI":"10.1109\/TGRS.2016.2569141","article-title":"Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. 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