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• W3114606989 abstract "In recent years, despite the tremendous progresses of object detection, small object detection has always been a challenge in the field of remote sensing. The main reason is that small objects cover few features that are easily lost during down-sampling. In this article, we propose a cross-layer attention network aiming to obtain stronger features of small objects for better detection. Specifically, we designed an up-sampling and down-sampling feature pyramid to obtain richer context information by bidirectionally fusing deep and shallow features, as well as skipping connections. Moreover, a cross-layer attention module is designed to obtain the nonlocal association of small objects in each layer, and further strengthen its representation ability through cross-layer integration and balance. Extensive experiments on the publicly available datasets (DIOR dataset and NWPUVHR-10 dataset) and the self-assembled datasets (SDOTA dataset and SDD dataset) show the excellent performance of our method compared with other detectors. Moreover, our method achieved 74.3% mAP on the public DIOR dataset without any tricks." @default.
• W3114606989 created "2021-01-05" @default.
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• W3114606989 date "2021-01-01" @default.
• W3114606989 modified "2023-10-16" @default.
• W3114606989 title "Cross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery" @default.
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• W3114606989 doi "https://doi.org/10.1109/jstars.2020.3046482" @default.
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