{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T22:20:39Z","timestamp":1774304439830,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSERC DG program"},{"name":"Chinese Scholarship Council"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask\u2013RCNN (Mask Region-based Convolutional Neural Networks) and Center\u2013Mask; 2. The independent semantic segmentation methods: U-net and Resnet\u2013U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.<\/jats:p>","DOI":"10.3390\/s23094444","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:12:11Z","timestamp":1682943131000},"page":"4444","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4569-9893","authenticated-orcid":false,"given":"Qiang","family":"Fang","sequence":"first","affiliation":[{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, av. de la M\u00e9decine, Qu\u00e9bec, QC G1V 0A6, Canada"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-0198-7439","authenticated-orcid":false,"given":"Clemente","family":"Ibarra-Castanedo","sequence":"additional","affiliation":[{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, av. de la M\u00e9decine, Qu\u00e9bec, QC G1V 0A6, Canada"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4415-4752","authenticated-orcid":false,"given":"Iv\u00e1n","family":"Garrido","sequence":"additional","affiliation":[{"name":"GeoTECH Group, Department of Natural Resources and Environmental Engineering, CINTECX, Universidade de Vigo, Campus Universitario de Vigo, 36310 Vigo, Spain"}]},{"given":"Yuxia","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Central South University, 932 Lushan South Road, Changsha 410083, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-8777-2008","authenticated-orcid":false,"given":"Xavier","family":"Maldague","sequence":"additional","affiliation":[{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, av. de la M\u00e9decine, Qu\u00e9bec, QC G1V 0A6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maldague, X.P.V. 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