基于加权可变形卷积的固体废物目标检测新方法

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Photogrammetric Engineering and Remote Sensing Pub Date : 2023-11-01 DOI:10.14358/pers.23-00024r2
Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin
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引用次数: 0

摘要

利用遥感影像对固体废物进行快速检测,对环境保护具有重要意义。近年来,基于深度学习的目标检测方法得到了广泛的研究。与飞机或建筑物等常规物体相比,固体废物通常具有难以区分边界的任意形状。本文提出了一种基于特征金字塔网络(FPN)模型的加权可变形卷积和全局上下文块的固体废物检测网络。所设计的特征提取结构有助于增强固体废物的边界特征和形状特征。在众所周知的光学遥感图像数据集和人工采集的固体废物数据集上验证了该方法的有效性。实验结果表明,该方法优于其他传统的目标检测方法,与FPN方法相比,最大改进幅度为5.27%。
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A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution
Rapid detection of solid waste with remote sensing images is of great significance for environmental protection. In recent years, deep learning-based object detection methods have been widely studied. In contrast to regular objects such as airplanes or buildings, solid wastes commonly h ave arbitrary shapes with difficult‐to‐distinguish boundaries. In this study, a solid waste detection network with a weighted deformable convolution and a global context block based on Feature Pyramid Network (FPN) model was proposed. The designed feature extraction structure can help to enhance the boundary and shape features of solid waste. The effectiveness of the proposed method was verified on the well-known DetectIon in Optical Remote sensing images data set and further on a solid waste data set, which was collected by the authors manually. The experimental results show that the proposed method outperforms other traditional object detection methods and a maximum improvement of 5.27% was obtained compared to the FPN method.
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来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
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