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• W4311187072 abstract "Solid waste is a widespread problem that is having a negative effect on the global environment. Owing to the ability of macroscopic observation, it is reasonable to believe that remote sensing could be an effective way to realize the detection and monitoring of solid waste. Solid waste is usually a mixture of various materials, with a randomly scattered distribution, which brings great difficulty to precise detection. In this article, we propose a deep learning network for solid waste detection in urban areas, aiming to realize the fast and automatic extraction of solid waste from the complicated and large-scale urban background. A novel dataset for solid waste detection was constructed by collecting 3192 images from Google Earth (with a resolution from 0.13 to 0.52 m), and then a location-guided key point network with multiple enhancements (LKN-ME) is proposed to perform the urban solid waste detection task. The LKN-ME method uses corner pooling and central convolution to capture the key points of an object. The location guidance is realized through constraining the key point locations situated of the annotated bounding box of an object. Multiple enhancements, including data mosaicing, an attention enhancement, and path aggregation, are integrated to improve the detection accuracy. The results show that the LKN-ME method can achieve a state-of-the-art AR <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>100</sub> (the average recall computed over 100 detections per image) of 71.8% and an average precision of 44.0% for the DSWD dataset, outperforming the classic object detection methods in solving the solid waste detection problem." @default.
• W4311187072 created "2022-12-24" @default.
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• W4311187072 date "2023-01-01" @default.
• W4311187072 modified "2023-10-14" @default.
• W4311187072 title "Solid Waste Detection in Cities Using Remote Sensing Imagery Based on a Location-Guided Key Point Network With Multiple Enhancements" @default.
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• W4311187072 doi "https://doi.org/10.1109/jstars.2022.3224555" @default.
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