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• W4313017766 abstract "Small vehicle detection is one of the remaining challenging task because the ambiguous appearance is against complex background interference. Consequently, in order to improve the performance of small vehicle detection from aerial optical remote sensing images, a novel adaptive local context (ALC) embedding way is designed and further introduced into an anchor free detection manner which is called ALC-Net, and in ALC-Net, it can adaptively set up the effective local context feature to improve keypoint description of small vehicles and boost the detection performance without adding extra prior information. Finally, several experiments are carried out on two widely used datasets (e.g., UCAS-AOD [1] and VEDAI [2]) and the results indicate that the proposed ALC-Net can exhibit the competitive small vehicle detection performance than other detectors." @default.
• W4313017766 created "2023-01-05" @default.
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• W4313017766 date "2022-07-17" @default.
• W4313017766 modified "2023-09-25" @default.
• W4313017766 title "Adaptive Local Context Embedding for Small Vehicle Detection from Aerial Optical Remote Sensing Images" @default.
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• W4313017766 doi "https://doi.org/10.1109/igarss46834.2022.9883080" @default.
• W4313017766 hasPublicationYear "2022" @default.
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