Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin
{"title":"基于加权可变形卷积的固体废物目标检测新方法","authors":"Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin","doi":"10.14358/pers.23-00024r2","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"49 11","pages":"0"},"PeriodicalIF":1.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution\",\"authors\":\"Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin\",\"doi\":\"10.14358/pers.23-00024r2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":49702,\"journal\":{\"name\":\"Photogrammetric Engineering and Remote Sensing\",\"volume\":\"49 11\",\"pages\":\"0\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering and Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.23-00024r2\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.23-00024r2","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
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.
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.