{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:16:35Z","timestamp":1759331795784,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138690"},{"type":"electronic","value":"9783031138706"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13870-6_25","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T09:03:13Z","timestamp":1660467793000},"page":"306-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modified Lightweight U-Net with Attention Mechanism for Weld Defect Detection"],"prefix":"10.1007","author":[{"given":"Lei","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiulin","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rujiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoqing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"issue":"5","key":"25_CR1","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1080\/174329313X13789830157500","volume":"7","author":"T Gang","year":"2002","unstructured":"Gang, T., Takahashi, Y., Wu, L.: Intelligent pattern recognition and diagnosis of ultrasonic inspection of welding defects based on neural network and information fusion. Sci. Technol. Weld. Joining 7(5), 314\u2013320 (2002)","journal-title":"Sci. Technol. Weld. Joining"},{"issue":"5","key":"25_CR2","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.rcim.2013.01.004","volume":"29","author":"M Dinham","year":"2013","unstructured":"Dinham, M., Gu, F.: Autonomous weld seam identification and localization using eye-in-hand stereo vision for robotic arc welding. Robot. Comput.-Integr. Manuf. 29(5), 288\u2013301 (2013)","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, L., Ye, Q., Yang, W., et al.: Weld Line Detection and Tracking via Spatial. Temporal Cascaded Hidden Markov Models and Cross Structured Light. IEEE Trans. Instr. Measur. 63(4), 742\u2013753 (2014)","DOI":"10.1109\/TIM.2013.2283139"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.rcim.2015.04.005","volume":"37","author":"Y He","year":"2015","unstructured":"He, Y.: Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model. Robot. Comput. Integ. Manuf. 37, 251\u2013261 (2015)","journal-title":"Robot. Comput. Integ. Manuf."},{"issue":"5","key":"25_CR5","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.imavis.2006.02.004","volume":"24","author":"V Leemans","year":"2016","unstructured":"Leemans, V., Destain, M.F.: Line cluster detection using a variant of the Hough transform for culture row localization. Image Vis. Comput. 24(5), 541\u2013550 (2016)","journal-title":"Image Vis. Comput."},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.optlastec.2017.09.037","volume":"99","author":"WJ Shao","year":"2018","unstructured":"Shao, W.J., Huang, Y., Zhang, Y.: A novel weld seam detection method for space weld seam of narrow butt joint in laser welding. Optics Laser Technol. 99, 39\u201351 (2018)","journal-title":"Optics Laser Technol."},{"issue":"8","key":"25_CR7","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.1007\/s11665-018-3502-8","volume":"27","author":"J Akram","year":"2018","unstructured":"Akram, J., Kalvala, P.R., Chalavadi, P., Misra, M.: Dissimilar metal weld joints of P91\/Ni alloy: microstructural characterization of HAZ of P91 and stress analysis at the weld interfaces. J. Mater. Eng. Perform. 27(8), 4115\u20134128 (2018). https:\/\/doi.org\/10.1007\/s11665-018-3502-8","journal-title":"J. Mater. Eng. Perform."},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.msea.2018.06.054","volume":"731","author":"A Kulkarni","year":"2018","unstructured":"Kulkarni, A., Dwivedi, D.K., Vasudevan, M.: Study of mechanism, microstructure and mechanical properties of activated flux TIG welded P91 Steel-P22 steel dissimilar metal joint. Mater. Sci. Eng. A. 731, 309\u2013323 (2018)","journal-title":"Mater. Sci. Eng. A."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Sirohi, S., Kumar,\u00a0S., Bhanu,\u00a0V., et al.: Study on the Variation in Mechanical Properties along the Dissimilar Weldments of P22 and P91 Steel. J. Mater. Eng. Perform.\u00a031, 2281\u20132296 (2022)","DOI":"10.1007\/s11665-021-06306-x"},{"issue":"10","key":"25_CR10","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1784\/insi.2012.55.10.535","volume":"55","author":"W Mu","year":"2013","unstructured":"Mu, W., Gao, J., Jiang, H., et al.: Automatic classification approach to weld defects based on PCA and SVM. Insight Non Destructive Testing & Condition Monitoring 55(10), 535\u2013539 (2013)","journal-title":"Insight Non Destructive Testing & Condition Monitoring"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Mu, W., Liu, G., Peng, L., et al.: A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA. In: International Conference on Advances in Mechanical Engineering & Industrial Informatics, pp. 1425\u20131429 (2015)","DOI":"10.2991\/ameii-15.2015.262"},{"issue":"1","key":"25_CR12","first-page":"53","volume":"27","author":"R Murugan","year":"2018","unstructured":"Murugan, R., Venugobal, P.R., Ramaswami, T.P., et al.: Studies on the effect of weld defect on the fatigue behavior of welded structures. China Weld. 27(1), 53\u201359 (2018)","journal-title":"China Weld."},{"key":"25_CR13","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1016\/j.jmapro.2020.04.059","volume":"56","author":"Y Cheng","year":"2020","unstructured":"Cheng, Y., Wang, Q., Jiao, W., et al.: Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding. J. Manuf. Process. 56, 908\u2013915 (2020)","journal-title":"J. Manuf. Process."},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.4028\/www.scientific.net\/AMM.752-753.1045","volume":"752\u2013753","author":"H Yazid","year":"2015","unstructured":"Yazid, H., Arof, H., Yazid, H., et al.: Weld detect identification using texture features and dynamic time warping. Appl. Mech. Mater. 752\u2013753, 1045\u20131050 (2015)","journal-title":"Appl. Mech. Mater."},{"issue":"14","key":"25_CR15","doi-asserted-by":"publisher","first-page":"3198","DOI":"10.3390\/s19143198","volume":"19","author":"A Wei","year":"2019","unstructured":"Wei, A., Chang, B., Xue, B., et al.: Research on the weld position detection method for sandwich structures from face-panel side based on backscattered X-ray. Sensors 19(14), 3198 (2019)","journal-title":"Sensors"},{"key":"25_CR16","doi-asserted-by":"publisher","first-page":"495","DOI":"10.4028\/www.scientific.net\/AMM.472.495","volume":"472","author":"PL Zhang","year":"2014","unstructured":"Zhang, P.L., Zhao, Z.Q., Wang, Y.P.: X-Ray testing of weld defect of automatic recognition and alarm technology research. Appl. Mech. Mater. 472, 495\u2013502 (2014)","journal-title":"Appl. Mech. Mater."},{"key":"25_CR17","first-page":"115","volume":"10","author":"J Ren","year":"2022","unstructured":"Ren, J., Wang, Y.: Overview of object detection algorithms using convolutional neural networks. J. Comput. Commun. 10, 115\u2013132 (2022)","journal-title":"J. Comput. Commun."},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Khumaidi, A., Yuniarno, E.M., Purnomo, M.H.: Welding defect classification based on convolution neural network (CNN) and Gaussian kernel. In: International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 261\u2013265 (2017)","DOI":"10.1109\/ISITIA.2017.8124091"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Chen, Z., Zhang, C., et al.: Weld defect detection based on deep learning method. In: IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1574\u20131579 (2019)","DOI":"10.1109\/COASE.2019.8842998"},{"issue":"1","key":"25_CR20","volume":"1894","author":"LF Zhang","year":"2021","unstructured":"Zhang, L.F., Gao, W.X., Wang, Z., et al.: Research on weld defect identification with X-ray based on convolutional neural network. J. Phys: Conf. Ser. 1894(1), 012071 (2021)","journal-title":"J. Phys: Conf. Ser."},{"key":"25_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103559","volume":"135","author":"M Liu","year":"2021","unstructured":"Liu, M., Xie, J., Hao, J., et al.: A lightweight and accurate recognition framework for signs of X-ray weld images. Comput Ind 135, 103559 (2021)","journal-title":"Comput Ind"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, Q., Zhi, Z., et al.: Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition. Welding in the World, Le Soudage Dans Le Monde, 65(4), pp. 731\u2013744 (2020)","DOI":"10.1007\/s40194-020-01027-6"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13870-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:57:00Z","timestamp":1709830620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13870-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138690","9783031138706"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13870-6_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}