{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:25:12Z","timestamp":1759537512294,"version":"build-2065373602"},"reference-count":45,"publisher":"World Scientific Pub Co Pte Ltd","issue":"18","funder":[{"DOI":"10.13039\/501100015401","name":"Key Research and Development Program of Shaanxi","doi-asserted-by":"crossref","award":["2024GX-ZDCYL-02-15"],"award-info":[{"award-number":["2024GX-ZDCYL-02-15"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p> Object detection in remote sensing images is a challenging task due to the objects with arbitrary orientations, scale variations and shape diversities. In response to the challenges of rotate-invariant feature extraction, multi-scale feature fusion, and widely discrepant bounding box regression, this paper proposes a Multi-grain Attention Based Object Detection Transformer named MGA-DETR, which is composed of three components, the Multi-grain Attention Pyramid Network (MGAPN), Center Position Correction Module (CPCM) and Adaptive Anchor Generation Module (AAGM). The designed MGAPN uses the multi-head attention mechanism to explore the critical sampling points, so that object features can be dynamically extracted. Based on the MGAPN, this paper uses the position of sampling points and attention weights to measure the object shape and proposes the Center Position Correction Module (CPCM) and Adaptive Anchor Generation Module (AAGM). The CPCM is proposed to adjust the object center for each layer in the multi-layer decoder structure, to provide accurate object positions for the bounding box regression. In the AAGM, according to the object shape in the last layer of the multi-layer decoder and the object center positions, the anchor adapted to the object shape is generated and provided to the detector to reduce the difficulty of regression. Extensive experiments are carried out on two publicly available datasets, DOTA and HRSC2016, achieving 71.88 mAP on DOTA and 89.1 on HRSC2016, demonstrating the effectiveness of the proposed MGA-DETR. <\/jats:p>","DOI":"10.1142\/s0218126625503566","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T04:52:58Z","timestamp":1747111978000},"source":"Crossref","is-referenced-by-count":0,"title":["MGA-DETR: Multi-Grain Attention for Remote Sensing Object Detection"],"prefix":"10.1142","volume":"34","author":[{"given":"Yingzhao","family":"Shao","sequence":"first","affiliation":[{"name":"The Key Laboratory of Smart Human\u2013Computer Interaction and Wearable Technology of Shaanxi Province, Department of Computer Science and Technology, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6135-0952","authenticated-orcid":false,"given":"Guo","family":"Yu","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Smart Human\u2013Computer Interaction and Wearable Technology of Shaanxi Province, Department of Computer Science and Technology, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4065-4052","authenticated-orcid":false,"given":"Pengfei","family":"Yang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Smart Human\u2013Computer Interaction and Wearable Technology of Shaanxi Province, Department of Computer Science and Technology, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Cheng","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Smart Human\u2013Computer Interaction and Wearable Technology of Shaanxi Province, Department of Computer Science and Technology, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Wireless Product Operation Division, Zhongxing Telecom Equipment, Xi'an 710065, P. 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