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Enhanced analysis of thermographic images for monitoring of district heat pipe networks
Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Datorseende. Termisk Syst Tekn AB, Diskettgatan 11 B, SE-58335 Linkoping, Sweden.ORCID-id: 0000-0002-6591-9400
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Termisk Syst Tekn AB, Diskettgatan 11 B, SE-58335 Linkoping, Sweden.ORCID-id: 0000-0002-6763-5487
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6096-3648
2016 (Engelska)Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 83, nr 2, s. 215-223Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We address two problems related to large-scale aerial monitoring of district heating networks. First, we propose a classification scheme to reduce the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps; by (a) using a building segmentation scheme in order to remove detections on buildings, and (b) to use a machine learning approach to classify the remaining detections as true or false leakages. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system. Second, we propose a method for characterization of leakages over time, i.e., repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss. We address the problem of finding trends in the degradation of pipe networks in order to plan for long-term maintenance, and propose a visualization scheme exploiting the consecutive data collections.

Ort, förlag, år, upplaga, sidor
Elsevier, 2016. Vol. 83, nr 2, s. 215-223
Nyckelord [en]
Remote thermography; Classification; Pattern recognition; District heating; Thermal infrared
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:liu:diva-133004DOI: 10.1016/j.patrec.2016.07.002ISI: 000386874800013OAI: oai:DiVA.org:liu-133004DiVA, id: diva2:1054676
Anmärkning

Funding Agencies|Swedish Research Council (Vetenskapsradet) through project Learning systems for remote thermography [621-2013-5703]; Swedish Research Council [2014-6227]

Tillgänglig från: 2016-12-08 Skapad: 2016-12-07 Senast uppdaterad: 2018-11-26
Ingår i avhandling
1. Detection and Tracking in Thermal Infrared Imagery
Öppna denna publikation i ny flik eller fönster >>Detection and Tracking in Thermal Infrared Imagery
2016 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy.

This thesis addresses the problem of detection and tracking in thermal infrared imagery. Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Indications oftheir popularity are recent benchmarks such as the annual Visual Object Tracking (VOT) challenges, the Object Tracking Benchmarks, the series of workshops on Performance Evaluation of Tracking and Surveillance (PETS), and the workshops on Change Detection. Benchmark results indicate that detection and tracking are still challenging problems.

A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true. The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis.

Detection and tracking are often treated as two separate problems. However, some tracking methods, e.g. template-based tracking methods, base their tracking on repeated specific detections. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of template-based tracking methods, and provides an overview of the first challenge on short-term,single-object tracking in thermal infrared video. Finally, two applications employing detection and tracking methods are presented.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2016. s. 66
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1744
Nyckelord
thermal, infrared, detection, tracking
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-126955 (URN)10.3384/lic.diva-126955 (DOI)978-91-7685-789-2 (ISBN)
Presentation
2016-05-10, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 16:16 (Engelska)
Opponent
Handledare
Forskningsfinansiär
Vetenskapsrådet, D0570301EU, FP7, Sjunde ramprogrammet, 312784EU, FP7, Sjunde ramprogrammet, 607567
Tillgänglig från: 2016-04-11 Skapad: 2016-04-08 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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