标题: Solid Waste Detection in Cities Using Remote Sensing Imagery Based on a Location-Guided Key Point Network With Multiple Enhancements

作者: Li, HF (Li, Huifang); Hu, C (Hu, Chao); Zhong, XR (Zhong, Xinrun); Zeng, C (Zeng, Chao); Shen, HF (Shen, Huanfeng)

来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING卷: 16页: 191-201 DOI: 10.1109/JSTARS.2022.3224555出版年: 2023

摘要: 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(100)(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.

作者关键词: Location-guided key point network; multiple enhancements; remote sensing; solid waste detection

地址: [Li, Huifang; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Li, Huifang; Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Hu, Chao; Zhong, Xinrun; Zeng, Chao] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址: Zeng, C (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: huifangli@whu.edu.cn; chaohu@whu.edu.cn; 2021202050019@whu.edu.cn; zengchao@whu.edu.cn;shenhf@whu.edu.cn

影响因子：4.715