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- W3209823299 abstract "In remotely sensed images, it is quite common to run into small objects, such as cars and small storage tanks. However, these small objects are quite easy to get ignored because of the positioning difficulty. Thus, small objects detection is very challenging for the remote sensing object detection task. In order to deal with this challenge, the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>optimized low coupling network</i> (OLCN) is proposed. First, a <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>low coupling robust regression</i> (LCRR) module improves the positioning accuracy to avoid small objects getting missed. Second, a <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>receptive field optimizing layer</i> (RFOL) is proposed to train better classifiers by providing more accurate <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>regions of interest</i> (RoIs). Experimental results on the public dataset HRRSD verify the effectiveness of the proposed OLCN. Small objects detection metric is improved from 5.70% of the baseline to 22.90% of the OLCN. Moreover, the proposed method has reached state-of-the-art performance on the HRRSD dataset." @default.
- W3209823299 created "2021-11-08" @default.
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- W3209823299 date "2022-01-01" @default.
- W3209823299 modified "2023-09-28" @default.
- W3209823299 title "OLCN: An Optimized Low Coupling Network for Small Objects Detection" @default.
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- W3209823299 doi "https://doi.org/10.1109/lgrs.2021.3122190" @default.
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