Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann
{"title":"从热图像中检测区域供热泄漏:异常检测方法的比较","authors":"Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann","doi":"10.1016/j.autcon.2024.105709","DOIUrl":null,"url":null,"abstract":"<div><div>District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection. While all publications boast high performance metrics, the methods are constructed around very different datasets, making a true comparison impossible.</div><div>Using a new set of aerial thermal images from two German cities, this paper implements, improves, and evaluates three anomaly detection methods for leakage detection: triangle-histogram-thresholding, saliency mapping, and local thresholding with filter kernels. The approaches are integrated into a software pipeline with globally applicable pre- and postprocessing, including vignetting correction. While all methods reliably detect thermal anomalies and are suitable for automated leakage detection, triangle-histogram-thresholding is the most robust.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods\",\"authors\":\"Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann\",\"doi\":\"10.1016/j.autcon.2024.105709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection. While all publications boast high performance metrics, the methods are constructed around very different datasets, making a true comparison impossible.</div><div>Using a new set of aerial thermal images from two German cities, this paper implements, improves, and evaluates three anomaly detection methods for leakage detection: triangle-histogram-thresholding, saliency mapping, and local thresholding with filter kernels. The approaches are integrated into a software pipeline with globally applicable pre- and postprocessing, including vignetting correction. While all methods reliably detect thermal anomalies and are suitable for automated leakage detection, triangle-histogram-thresholding is the most robust.</div></div>\",\"PeriodicalId\":8660,\"journal\":{\"name\":\"Automation in Construction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automation in Construction\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S092658052400445X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092658052400445X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods
District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection. While all publications boast high performance metrics, the methods are constructed around very different datasets, making a true comparison impossible.
Using a new set of aerial thermal images from two German cities, this paper implements, improves, and evaluates three anomaly detection methods for leakage detection: triangle-histogram-thresholding, saliency mapping, and local thresholding with filter kernels. The approaches are integrated into a software pipeline with globally applicable pre- and postprocessing, including vignetting correction. While all methods reliably detect thermal anomalies and are suitable for automated leakage detection, triangle-histogram-thresholding is the most robust.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.