{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:10:10Z","timestamp":1772694610175,"version":"3.50.1"},"reference-count":107,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T00:00:00Z","timestamp":1762041600000},"content-version":"vor","delay-in-days":305,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.070906","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T08:39:42Z","timestamp":1760517582000},"page":"4173-4201","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["X-Ray Techniques for Defect Detection in Industrial Components and Materials: A Review"],"prefix":"10.32604","volume":"85","author":[{"given":"Xin","family":"Wen","sequence":"first","affiliation":[]},{"given":"Siru","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kechen","family":"Song","sequence":"additional","affiliation":[]},{"given":"Han","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xingjie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"12051","DOI":"10.3390\/app112412051","article-title":"Extracting weld bead shapes from radiographic testing images with U-Net","volume":"11","author":"Jin","year":"2021","journal-title":"Appl Sci"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","author":"Liu","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"3126","DOI":"10.1109\/TUFFC.2021.3081750","article-title":"Automated defect detection from ultrasonic images using deep learning","volume":"68","author":"Medak","year":"2021","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TIM.2012.2218677","article-title":"Automatic defect detection on hot-rolled flat steel products","volume":"62","author":"Ghorai","year":"2012","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"183192","DOI":"10.1109\/ACCESS.2020.3029127","article-title":"A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry","volume":"8","author":"Ebayyeh","year":"2020","journal-title":"IEEE Access"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"9508","DOI":"10.3390\/app11209508","article-title":"A review on machine and deep learning for semiconductor defect classification in scanning electron microscope images","volume":"11","author":"L\u00f3pez de la Rosa","year":"2021","journal-title":"Appl Sci"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1061\/(ASCE)0899-1561(2002)14:2(122)","article-title":"Characterization of air void distribution in asphalt mixes using X-ray computed tomography","volume":"14","author":"Masad","year":"2002","journal-title":"J Mater Civ Eng"},{"key":"ref8","series-title":"Proceedings of the IEEE 9th International Colloquium on Signal Processing and Its Applications (CSPA); 2013 Mar 8\u201310; Kuala Lumpur, Malaysia","first-page":"67","article-title":"Weld defect features extraction on digital radiographic image using Chan-Vese model","author":"Abd Halim"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00170-008-1720-1","article-title":"Application of a new image segmentation method to detection of defects in castings","volume":"43","author":"Tang","year":"2009","journal-title":"Int J Adv Manuf Technol"},{"key":"ref10","series-title":"Proceedings of the 2024 International Conference on Cyber-Physical Social Intelligence (ICCSI); 2024 Dec 18\u201320; Doha, Qatar","first-page":"1","article-title":"A survey of industrial surface defect detection based on deep learning","author":"Haobo"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.53375\/ijecer.2023.320","article-title":"RIAWELC: a Novel dataset of radiographic images for automatic weld defects classification","volume":"3","author":"Totino","year":"2023","journal-title":"Int J Electr Comput Eng Res"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"102764","DOI":"10.1016\/j.ndteint.2022.102764","article-title":"Defects detection in weld joints based on visual attention and deep learning","volume":"133","author":"Ji","year":"2023","journal-title":"NDT E Int"},{"key":"ref13","series-title":"Proceedings of the 2017 IEEE International Conference on Big Data (Big Data); 2017 Dec 11\u201314; Boston, MA, USA","first-page":"1726","article-title":"Automatic localization of casting defects with convolutional neural networks","author":"Ferguson"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ndteint.2008.06.005","article-title":"Flaw detection in radiographic weldment images using morphological watershed segmentation technique","volume":"42","author":"Alaknanda","year":"2009","journal-title":"NDT E Int"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1109\/TASE.2020.3039115","article-title":"A random forest-based automatic inspection system for aerospace welds in X-ray images","volume":"18","author":"Dong","year":"2020","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"ref16","unstructured":"Cozma A, Harris L, Qi H, Ji P, Guo W, Yuan S. Defect detection in tire X-ray images: conventional methods meet deep structures. arXiv:2402.18527. 2024."},{"key":"ref17","series-title":"Proceedings of the International Conference on Intelligent Computing","first-page":"53","article-title":"A welding defect identification approach in X-ray images based on deep convolutional neural networks","author":"Wang","year":"2019"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s40194-022-01281-w","article-title":"X-ray weld defect detection based on AF-RCNN","volume":"66","author":"Liu","year":"2022","journal-title":"Weld World"},{"key":"ref19","first-page":"1","article-title":"STMA-Net: a spatial transformation-based multiscale attention network for complex defect detection with X-ray images","volume":"73","author":"Zuo","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref20","series-title":"Proceedings of the International Forum on Digital TV and Wireless Multimedia Communications","first-page":"215","article-title":"Weld defect images classification with VGG16-based neural network","author":"Liu","year":"2017"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s10921-021-00823-4","article-title":"Inspection of welding defect based on multi-feature fusion and a convolutional network","volume":"40","author":"Yang","year":"2021","journal-title":"J Nondestruct Eval"},{"key":"ref22","series-title":"Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE); 2019 Aug 22\u201326; Vancouver, BC, Canada","first-page":"1574","article-title":"Weld defect detection based on deep learning method","author":"Zhang"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"102549","DOI":"10.1016\/j.ndteint.2021.102549","article-title":"Automatic defect detection from X-ray scans for aluminum conductor composite core wire based on classification neutral network","volume":"124","author":"Hu","year":"2021","journal-title":"NDT E Int"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"102899","DOI":"10.1016\/j.ndteint.2023.102899","article-title":"Welding defects classification by weakly supervised semantic segmentation","volume":"138","author":"Zhang","year":"2023","journal-title":"NDT E Int"},{"key":"ref25","series-title":"Proceedings of the 2024 7th International Conference on Robotics, Control and Automation Engineering (RCAE); 2024 Mar 15\u201317; Beijing, China","first-page":"429","article-title":"Instance segmentation based non-destructive inspection of high-voltage cable defects","author":"Shao"},{"key":"ref26","series-title":"Proceedings of the Canadian Institute for Non-destructive Evaluation (CINDE); 2019 Jun 18\u201320; Edmonton, AB, Canada","first-page":"18","article-title":"Automatic defect detection for X-ray inspection: Identifying defects with deep convolutional network","author":"Tokime"},{"key":"ref27","first-page":"1","article-title":"Development of high accuracy welding defect detection technique for X-ray images","volume":"59","author":"Matsumoto","year":"2022","journal-title":"Mitsubishi Heavy Ind Tech Rev"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jmapro.2025.02.039","article-title":"A comprehensive review of welding defect recognition from X-ray images","volume":"140","author":"Wang","year":"2025","journal-title":"J Manuf Process"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"9436","DOI":"10.1109\/TII.2022.3228702","article-title":"Basic-class and cross-class hybrid feature learning for class-imbalanced weld defect recognition","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Trans Ind Inform"},{"key":"ref30","first-page":"e230008","article-title":"Pore segmentation in industrial radiographic images using adaptive thresholding and Morphological analysis","author":"Ram\u00edrez","year":"2023","journal-title":"Trends Agric Environ Sci"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1134\/S1061830919020074","article-title":"Segmentation of weld defects using multiphase level set by the piecewise-smooth Mumford-Shah model","volume":"55","author":"Ramou","year":"2019","journal-title":"Russ J Nondestruct Test"},{"key":"ref32","series-title":"Proceedings of the 2011 International Conference on Control, Automation and Systems Engineering (CASE); 2011 Jul 30\u201331; Singapore","first-page":"1","article-title":"Automatic defect detection method for the steel cord conveyor belt based on its X-ray images","author":"Li"},{"key":"ref33","series-title":"Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE); 2019 May 5\u20138; Edmonton, AB, Canada","first-page":"1","article-title":"Defect detection from X-ray images using a three-stage deep learning algorithm","author":"Ren"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ndteint.2016.11.003","article-title":"Automated detection of welding defects in pipelines from radiographic images DWDI","volume":"86","author":"Boaretto","year":"2017","journal-title":"NDT E Int"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"102144","DOI":"10.1016\/j.ndteint.2019.102144","article-title":"Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning","volume":"107","author":"Du","year":"2019","journal-title":"NDT E Int"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TED.2009.2031001","article-title":"Very large area CMOS active-pixel sensor for digital radiography","volume":"56","author":"Farrier","year":"2009","journal-title":"IEEE Trans Electron Devices"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.nima.2005.01.310","article-title":"Development of a lens-coupled CMOS detector for an X-ray inspection system","volume":"545","author":"Kim","year":"2005","journal-title":"Nucl Instrum Methods Phys Res Sect A"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10921-015-0315-7","article-title":"GDXray: the database of X-ray images for nondestructive testing","volume":"34","author":"Mery","year":"2015","journal-title":"J Nondestruct Eval"},{"key":"ref39","series-title":"Proceedings of the 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD); 2018 Jul 28\u201330;","first-page":"1051","article-title":"WDXI: the dataset of X-ray image for weld defects","author":"Guo"},{"key":"ref40","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":"Appl Artif Intell"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1080\/08839514.2021.1975391","article-title":"Deep learning based steel pipe weld defect detection","volume":"35","author":"Yang","year":"2021","journal-title":"Appl Artif Intell"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"11171","DOI":"10.1109\/TII.2024.3399934","article-title":"A rapid screening method for suspected defects in steel pipe welds by combining correspondence mechanism and normalizing flow","volume":"20","author":"Cui","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s10921-025-01186-w","article-title":"SWRD: a dataset of radiographic image of seam weld for defect detection","volume":"44","author":"Zhao","year":"2025","journal-title":"J Nondestruct Eval"},{"key":"ref44","first-page":"1","article-title":"Fine-grained tiny defect detection in spiral welds: a joint framework combining semantic discrimination and contrast transformation","volume":"74","author":"Cui","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"118052","DOI":"10.1016\/j.measurement.2025.118052","article-title":"TEGDNet: texture enhancement guided detection network for spiral welded pipeline defect detection","volume":"256","author":"Zhang","year":"2025","journal-title":"Measurement"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"116119","DOI":"10.1016\/j.measurement.2024.116119","article-title":"Deep learning-based detection of internal defect types and their grades in high-pressure aluminum castings","volume":"242","author":"Parlak","year":"2025","journal-title":"Measurement"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"166342","DOI":"10.1016\/j.ijleo.2021.166342","article-title":"An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-radiography image","volume":"231","author":"Malarvel","year":"2021","journal-title":"Optik"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"125929","DOI":"10.1109\/ACCESS.2019.2927258","article-title":"Automatic welding defect detection of X-ray images by using cascade adaboost with penalty term","volume":"7","author":"Duan","year":"2019","journal-title":"IEEE Access"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"102345","DOI":"10.1016\/j.ndteint.2020.102345","article-title":"Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays","volume":"116","author":"Yu","year":"2020","journal-title":"NDT E Int"},{"key":"ref50","series-title":"Proceedings of the 16th World Conference on Non-Destructive Testing (WCNDT 2004); 2004 Aug 30\u2013Sep 3; Montreal, QC, Canada","article-title":"Automated defect detection in aluminium castings and welds using neuro-fuzzy classifiers","author":"Hernandez"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.3390\/ma15103697","article-title":"A review of non-destructive testing (NDT) techniques for defect detection: application to fusion welding and future wire arc additive manufacturing processes","volume":"15","author":"Shaloo","year":"2022","journal-title":"Materials"},{"key":"ref52","series-title":"Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC); 2018 Jun 27\u201329; Chongqing, China","first-page":"138","article-title":"The defect detection algorithm for tire X-ray images based on deep learning","author":"Zhu"},{"key":"ref53","series-title":"Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering; 2009 Mar 31\u2013Apr 2; Los Angeles, CA, USA","first-page":"111","article-title":"Welding defect detection by segmentation of radiographic images,","author":"Mahmoudi"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s10921-015-0305-9","article-title":"Classification of welding flaws in gamma radiography images based on multi-scale wavelet packet feature extraction using support vector machine","volume":"34","author":"El-Tokhy","year":"2015","journal-title":"J Nondestruct Eval"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1140\/epjp\/s13360-021-01652-0","article-title":"Using nonlocal operators for measuring dimensions of defects in radiograph of welded objects","volume":"136","author":"Movafeghi","year":"2021","journal-title":"Eur Phys J Plus"},{"key":"ref56","first-page":"147","article-title":"Application of frequency domain processing to X-ray radiographic images of welding defects","volume":"15","author":"Rajab","year":"2007","journal-title":"J X-Ray Sci Technol"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/a16020095","article-title":"Defect detection methods for industrial products using deep learning techniques: a review","volume":"16","author":"Saberironaghi","year":"2023","journal-title":"Algorithms"},{"key":"ref58","series-title":"Proceedings of the 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET); 2018 Mar 24\u201326; Hammamet, Tunisia","first-page":"11","article-title":"New procedure for weld defect detection based-gabor filter","author":"Ajmi"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ndteint.2011.10.008","article-title":"Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence","volume":"46","author":"Shao","year":"2012","journal-title":"NDT E Int"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.dsp.2017.05.014","article-title":"Anisotropic diffusion based denoising on X-radiography images to detect weld defects","volume":"68","author":"Malarvel","year":"2017","journal-title":"Digit Signal Process"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1140\/epjp\/s13360-020-00119-y","article-title":"Interlaced bilateral filtering and wavelet thresholding for flaw detection in the radiography of weldments","volume":"135","author":"Yahaghi","year":"2020","journal-title":"Eur Phys J Plus"},{"key":"ref62","first-page":"352","article-title":"Application of an improved watershed algorithm in welding image segmentation","volume":"47","author":"Wang","year":"2011","journal-title":"Trans China Weld Inst"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/S1007-0214(06)70255-3","article-title":"Automatic defect detection in X-ray images using image data fusion","volume":"11","author":"Tian","year":"2006","journal-title":"Tsinghua Sci Technol"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.microrel.2016.10.011","article-title":"X-ray inspection of TSV defects with self-organizing map network and Otsu algorithm","volume":"67","author":"Shen","year":"2016","journal-title":"Microelectron Reliab"},{"key":"ref65","series-title":"Proceedings of the 2017 Trends in Industrial Measurement and Automation (TIMA); 2017 Dec 7\u20139; Chennai, India","first-page":"1","article-title":"Spatial smoothing based segmentation method for internal defect detection in X-ray images of casting components","author":"Kamalakannan"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.patcog.2009.08.002","article-title":"An efficient local Chan-Vese model for image segmentation","volume":"43","author":"Wang","year":"2010","journal-title":"Pattern Recogn"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1016\/j.matpr.2020.12.806","article-title":"Segmentation of X-ray image for welding defects detection using an improved Chan-Vese model","volume":"42","author":"Abdelkader","year":"2021","journal-title":"Mater Today Proc"},{"key":"ref68","doi-asserted-by":"crossref","first-page":"155","DOI":"10.4028\/p-w863h3","article-title":"Welding defects detection in radiographic images using an improved denoising technique combined with an enhanced Chan-Vese model","volume":"60","author":"Abdelkader","year":"2022","journal-title":"Int J Eng Res Afr"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"102599","DOI":"10.1016\/j.ndteint.2021.102599","article-title":"Accurate segmentation of weld defects with horizontal shapes","volume":"126","author":"Radi","year":"2022","journal-title":"NDT E Int"},{"key":"ref70","series-title":"Proceedings of the 2011 4th International Congress on Image and Signal Processing (CISP); 2011 Oct 15\u201317; Shanghai, China","first-page":"1842","article-title":"Automatic weld defect detection in real-time X-ray images based on support vector machine","author":"Shao"},{"key":"ref71","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s10921-021-00801-w","article-title":"An autonomous technique for multi class weld imperfections detection and classification by support vector machine","volume":"40","author":"Patil","year":"2021","journal-title":"J Nondestruct Eval"},{"key":"ref72","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.promfg.2019.12.065","article-title":"Research on approaches for computer aided detection of casting defects in X-ray images with feature engineering and machine learning","volume":"37","author":"Wu","year":"2019","journal-title":"Procedia Manuf"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"01017","DOI":"10.1051\/e3sconf\/202455201017","article-title":"Identification of weld sub-surface defects by radiographic images using texture features","volume":"552","author":"Ramana","year":"2024","journal-title":"E3S Web Conf"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"7430","DOI":"10.1109\/JSEN.2023.3247006","article-title":"LF-YOLO: a lighter and faster YOLO for weld defect detection of X-ray image","volume":"23","author":"Liu","year":"2023","journal-title":"IEEE Sens J"},{"key":"ref75","doi-asserted-by":"crossref","first-page":"108045","DOI":"10.1016\/j.engappai.2024.108045","article-title":"Research on X-ray weld seam defect detection and size measurement method based on neural network self-optimization","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"4519","DOI":"10.3390\/app15084519","article-title":"Research on X-ray weld defect detection of steel pipes by integrating ECA and EMA dual attention mechanisms","volume":"15","author":"Su","year":"2025","journal-title":"Appl Sci"},{"key":"ref77","first-page":"1","article-title":"A complex welding defect detection method based on Active Learning in pipeline transportation system","volume":"74","author":"Zuo","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10921-021-00842-1","article-title":"Automated defect recognition of castings defects using neural networks","volume":"41","author":"Garc\u00eda P\u00e9rez","year":"2022","journal-title":"J Nondestruct Eval"},{"key":"ref79","series-title":"Proceedings of the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS); 2023 Sep 22\u201324; Beijing, China","first-page":"1","article-title":"An effective detection method for complex weld defects based on adaptive feature pyramid","author":"Zuo"},{"key":"ref80","series-title":"Proceedings of the 2021 IEEE Far East NDT New Technology & Application Forum (FENDT); 2021; Kunming, China","first-page":"58","article-title":"Automated detection of defects with casting DR image based on deep learning","author":"Fu"},{"key":"ref81","doi-asserted-by":"crossref","first-page":"103059","DOI":"10.1016\/j.ndteint.2024.103059","article-title":"Zoom in on the target network for the prediction of defective images and welding defects\u2019 location","volume":"143","author":"Wang","year":"2024","journal-title":"NDT E Int"},{"key":"ref82","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TMECH.2023.3327713","article-title":"An X-ray-based automatic welding defect detection method for special equipment system","volume":"29","author":"Zuo","year":"2023","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"106821","DOI":"10.1016\/j.compeleceng.2020.106821","article-title":"Self-attention guided model for defect detection of aluminium alloy casting on X-ray image","volume":"88","author":"Wang","year":"2020","journal-title":"Comput Electr Eng"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"114174","DOI":"10.1016\/j.knosys.2025.114174","article-title":"Multiscale welding defect detection method based on image adaptive enhancement","volume":"327","author":"Cheng","year":"2025","journal-title":"Knowl Based Syst"},{"key":"ref85","doi-asserted-by":"crossref","first-page":"1574350","DOI":"10.1155\/2020\/1574350","article-title":"Deep learning technology for weld defects classification based on transfer learning and activation features","volume":"2020","author":"Ajmi","year":"2020","journal-title":"Adv Mater Sci Eng"},{"key":"ref86","series-title":"Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus); 2021 Jan 26\u201329; St. Petersburg, Russia","first-page":"1641","article-title":"Classification of defects in welds using a convolution neural network","author":"Nazarov"},{"key":"ref87","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ndteint.2019.05.002","article-title":"Deep neural networks based approach for welded joint detection of oil pipelines in radiographic images with Double Wall Double Image exposure","volume":"105","author":"Suyama","year":"2019","journal-title":"NDT E Int"},{"key":"ref88","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.measurement.2020.108736","article-title":"Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation","volume":"170","author":"Jiang","year":"2021","journal-title":"Measurement"},{"key":"ref89","doi-asserted-by":"crossref","first-page":"108379","DOI":"10.1016\/j.engappai.2024.108379","article-title":"Synthetic data augmentation for high-resolution X-ray welding defect detection and classification based on a small number of real samples","volume":"133","author":"Li","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref90","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.measurement.2018.09.011","article-title":"Deep features based on a DCNN model for classifying imbalanced weld flaw types","volume":"131","author":"Hou","year":"2019","journal-title":"Measurement"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"6422","DOI":"10.3390\/s23146422","article-title":"Automated categorization of multiclass welding defects using the X-ray image augmentation and convolutional neural network","volume":"23","author":"Say","year":"2023","journal-title":"Sensors"},{"key":"ref92","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015 Jun 7\u201312; Boston, MA, USA","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long"},{"key":"ref93","doi-asserted-by":"crossref","first-page":"102435","DOI":"10.1016\/j.ndteint.2021.102435","article-title":"An automatic welding defect location algorithm based on deep learning","volume":"120","author":"Yang","year":"2021","journal-title":"NDT E Int"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"5437","DOI":"10.1109\/ACCESS.2024.3521220","article-title":"Combining multi-scale U-Net with transformer for welding defect detection of oil\/gas pipeline","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1134\/S1061830924602903","article-title":"A novel method for segmentation and detection of weld defects in UHV equipment based on multiscale feature fusion","volume":"60","author":"Zong","year":"2024","journal-title":"Russ J Nondestruct Test"},{"key":"ref96","doi-asserted-by":"crossref","first-page":"6312","DOI":"10.1038\/s41598-024-56794-9","article-title":"A new method for deep learning detection of defects in X-ray images of pressure vessel welds","volume":"14","author":"Wang","year":"2024","journal-title":"Sci Rep"},{"key":"ref97","doi-asserted-by":"crossref","first-page":"102597","DOI":"10.1016\/j.ndteint.2021.102597","article-title":"Approach to weld segmentation and defect classification in radiographic images of pipe welds","volume":"127","author":"Golodov","year":"2022","journal-title":"NDT E Int"},{"key":"ref98","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.jmapro.2023.05.058","article-title":"Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT","volume":"99","author":"Xu","year":"2023","journal-title":"J Manuf Process"},{"key":"ref99","first-page":"1","article-title":"An automatic deep segmentation network for pixel-level welding defect detection","volume":"71","author":"Yang","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref100","doi-asserted-by":"crossref","first-page":"108338","DOI":"10.1016\/j.knosys.2022.108338","article-title":"A nondestructive automatic defect detection method with pixelwise segmentation","volume":"242","author":"Yang","year":"2022","journal-title":"Knowl-Based Syst"},{"key":"ref101","doi-asserted-by":"crossref","first-page":"12912","DOI":"10.1109\/TIE.2020.3047060","article-title":"Automatic defect segmentation in X-ray images based on deep learning. IEEE Trans","volume":"68","author":"Du","year":"2020","journal-title":"Ind Electron"},{"key":"ref102","series-title":"Proceedings of the 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP); 2024 Apr 19\u201321; Xi\u2019an, China","first-page":"1564","article-title":"Convolutional neural network based defect detection in small diameter pipe weld","author":"Guo"},{"key":"ref103","series-title":"Proceedings of the 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP); 2024 May 24\u201326; Hangzhou, China","first-page":"665","article-title":"Weld defect segmentation algorithm based on improved U-net","author":"Wang"},{"key":"ref104","doi-asserted-by":"crossref","first-page":"104123","DOI":"10.1016\/j.compind.2024.104123","article-title":"Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network","volume":"161","author":"Liu","year":"2024","journal-title":"Comput Ind"},{"key":"ref105","doi-asserted-by":"crossref","first-page":"17849","DOI":"10.1038\/s41598-025-02421-0","article-title":"High resolution weld semantic defect detection algorithm based on integrated double U structure","volume":"15","author":"Li","year":"2025","journal-title":"Sci Rep"},{"key":"ref106","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1109\/TMECH.2024.3408337","article-title":"An X-ray-based multiexpert inspection method for automatic welding defect assessment in intelligent pipeline system. IEEE\/ASME","volume":"30","author":"Zuo","year":"2025","journal-title":"Trans Mechatron"},{"key":"ref107","doi-asserted-by":"crossref","first-page":"126386","DOI":"10.1016\/j.eswa.2025.126386","article-title":"On the effect of the attention mechanism for automatic welding defects detection based on deep learning","volume":"268","author":"Wang","year":"2025","journal-title":"Expert Syst Appl"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-3\/TSP_CMC_70906\/TSP_CMC_70906.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:26:01Z","timestamp":1763346361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n3\/64205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":107,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.070906","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-07-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-23","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}