Classifying district heating network leakages in aerial thermal imagery
2014 (English)Conference paper, Presentation (Other academic)
In this paper we address the problem of automatically detecting leakages in underground pipes of district heating networks from images captured by an airborne thermal camera. The basic idea is to classify each relevant image region as a leakage if its temperature exceeds a threshold. This simple approach yields a significant number of false positives. We propose to address this issue by machine learning techniques and provide extensive experimental analysis on real-world data. The results show that this postprocessing step significantly improves the usefulness of the system.
Place, publisher, year, edition, pages
, Svenska sällskapet för automatiserad bildanalys (SSBA)
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-127540OAI: oai:DiVA.org:liu-127540DiVA: diva2:925813
Swedish Symposium on Image Analysis