{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:45:10Z","timestamp":1764175510792,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Council of Canada (NSERC)","award":["496439-2017","FR49395"],"award-info":[{"award-number":["496439-2017","FR49395"]}]},{"name":"Mitacs Acceleration program","award":["496439-2017","FR49395"],"award-info":[{"award-number":["496439-2017","FR49395"]}]},{"name":"Canada Research Chair in Multi-polar Infrared Vision (MIVIM)","award":["496439-2017","FR49395"],"award-info":[{"award-number":["496439-2017","FR49395"]}]},{"name":"Canada Foundation for Innovation","award":["496439-2017","FR49395"],"award-info":[{"award-number":["496439-2017","FR49395"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.<\/jats:p>","DOI":"10.3390\/info15010016","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T02:58:12Z","timestamp":1703645892000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6996-4340","authenticated-orcid":false,"given":"Bata","family":"Hena","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"},{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5015-8696","authenticated-orcid":false,"given":"Ziang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"},{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Perron","sequence":"additional","affiliation":[{"name":"GI Lab Solutions, 475 Pruneau Ave, Quebec City, QC G1M 2J8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-7439","authenticated-orcid":false,"given":"Clemente Ibarra","family":"Castanedo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"},{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8777-2008","authenticated-orcid":false,"given":"Xavier","family":"Maldague","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"},{"name":"Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Universit\u00e9 Laval, 1065, Ave de la M\u00e9decine, Quebec City, QC G1V 0A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"ref_1","first-page":"526","article-title":"YOLOv4 Object Detection Model for Nondestructive Radiographic Testing in Aviation Maintenance Tasks","volume":"60","author":"Chen","year":"2021","journal-title":"AIAA J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"349","DOI":"10.33564\/IJEAST.2022.v07i01.054","article-title":"Non-Destructive Testing for Quality Control in Automotive Industry","volume":"7","author":"Misokefalou","year":"2022","journal-title":"IJEAST"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105636","DOI":"10.1016\/j.engappai.2022.105636","article-title":"Deep Learning-Based Detection of Aluminum Casting Defects and Their Types","volume":"118","author":"Parlak","year":"2023","journal-title":"Eng. 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