{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:36:54Z","timestamp":1753882614953,"version":"3.41.2"},"reference-count":35,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","funder":[{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20224BAB202033"],"award-info":[{"award-number":["20224BAB202033"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013064","name":"Key Research and Development Program of Jiangxi Province","doi-asserted-by":"publisher","award":["20192BBE50015"],"award-info":[{"award-number":["20192BBE50015"]}],"id":[{"id":"10.13039\/501100013064","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p> Groove edge detection is the prerequisite for weld seam deviation identification. A welding groove edge detection method based on transfer learning is presented as a solution to the inaccuracy of the conventional image processing method for extracting the edge of the welding groove. DenseNet and MobileNetV2 are used as feature extractors for transfer learning. Dense-Mobile Net is constructed using the skip connections structure and depthwise separable convolution. The Dense-Mobile Net training procedure consists of two stages: pre-training and model fusion fine-tuning. Experiments demonstrate that the proposed model accurately detects groove edges in MAG welding images. Using MIG welding images and the Pascal VOC2012 dataset to evaluate the generalization ability of the model, the relevant indicators are greater than those of Support Vector Machine (SVM), Fully Convolutional Networks (FCN), and UNet. The average single-frame detection time of the proposed model is 0.14 s, which meets the requirements of industrial real-time performance. <\/jats:p>","DOI":"10.1142\/s021800142351014x","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T16:18:05Z","timestamp":1690561085000},"source":"Crossref","is-referenced-by-count":1,"title":["Welding Groove Edge Detection Method Using Lightweight Fusion Model Based on Transfer Learning"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-0583","authenticated-orcid":false,"given":"Bo","family":"Guo","sequence":"first","affiliation":[{"name":"Nanchang Key Laboratory of Welding Robot & Intelligent Technology, Nanchang Institute of Technology, Nanchang 330099, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4583-4193","authenticated-orcid":false,"given":"Lanxiang","family":"Rao","sequence":"additional","affiliation":[{"name":"Jiangxi Science and Technology Infrastructure Platform Center, Nanchang, 330003, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9576-2502","authenticated-orcid":false,"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"Nanchang Key Laboratory of Welding Robot & Intelligent Technology, Nanchang Institute of Technology, Nanchang 330099, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9608-7128","authenticated-orcid":false,"given":"Yuwen","family":"Li","sequence":"additional","affiliation":[{"name":"Nanchang Key Laboratory of Welding Robot & Intelligent Technology, Nanchang Institute of Technology, Nanchang 330099, P. R. China"}]},{"given":"Wen","family":"Yang","sequence":"additional","affiliation":[{"name":"Jianglian Heavy Industry Group Co., Ltd, Nanchang 330096, P. R. China"}]},{"given":"Jianmin","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangxi Hengda Hi-Tech Co., Ltd, Nanchang 330096, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"S021800142351014XBIB001","first-page":"1","volume":"71","author":"Liu T.","year":"2022","journal-title":"IEEE Trans. Instrum."},{"key":"S021800142351014XBIB002","doi-asserted-by":"crossref","first-page":"16339","DOI":"10.1109\/JSEN.2022.3189681","volume":"22","author":"Su N.","year":"2022","journal-title":"IEEE Sens. J."},{"key":"S021800142351014XBIB003","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.3390\/s22072555","volume":"22","author":"Su N.","year":"2022","journal-title":"J. Sensors."},{"key":"S021800142351014XBIB004","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.3390\/s22062117","volume":"22","author":"Zhu C.","year":"2022","journal-title":"Sensors (Basel)"},{"key":"S021800142351014XBIB005","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jmatprotec.2016.12.029","volume":"243","author":"Zhu J.","year":"2017","journal-title":"J. Mater. Process. Technol."},{"key":"S021800142351014XBIB006","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s00170-018-2732-0","volume":"100","author":"Yang L.","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"S021800142351014XBIB007","doi-asserted-by":"crossref","first-page":"108388","DOI":"10.1016\/j.optlastec.2022.108388","volume":"155","author":"Li W.","year":"2022","journal-title":"Opt. Laser Technol."},{"key":"S021800142351014XBIB008","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1007\/s00170-016-8721-2","volume":"87","author":"Guo B.","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"S021800142351014XBIB009","doi-asserted-by":"crossref","first-page":"104582","DOI":"10.1016\/j.autcon.2022.104582","volume":"143","author":"Liu J.","year":"2022","journal-title":"Autom. Constr."},{"key":"S021800142351014XBIB010","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s00170-021-07380-0","volume":"116","author":"Zhao Z.","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"S021800142351014XBIB011","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1364\/AO.389730","volume":"59","author":"Zou Y.","year":"2020","journal-title":"Appl. Opt."},{"issue":"08","key":"S021800142351014XBIB012","doi-asserted-by":"crossref","first-page":"1859014","DOI":"10.1142\/S0218001418590140","volume":"32","author":"Ding D.","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"issue":"08","key":"S021800142351014XBIB013","doi-asserted-by":"crossref","first-page":"2052004","DOI":"10.1142\/S0218001420520047","volume":"34","author":"Li Y.","year":"2020","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"issue":"13","key":"S021800142351014XBIB014","doi-asserted-by":"crossref","first-page":"2350236","DOI":"10.1142\/S0218126623502365","volume":"32","author":"Xu W.","year":"2023","journal-title":"J. Circuits Syst. Comput."},{"key":"S021800142351014XBIB015","doi-asserted-by":"crossref","first-page":"110129","DOI":"10.1016\/j.measurement.2021.110129","volume":"186","author":"Yang G.","year":"2021","journal-title":"Measurement"},{"key":"S021800142351014XBIB016","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.3390\/met12081365","volume":"12","author":"Lu J.","year":"2022","journal-title":"Metals"},{"key":"S021800142351014XBIB017","doi-asserted-by":"crossref","first-page":"4130","DOI":"10.3390\/s22114130","volume":"22","author":"Wang B.","year":"2022","journal-title":"Sensors (Basel)"},{"key":"S021800142351014XBIB018","first-page":"27","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Yosinski J.","year":"2014"},{"key":"S021800142351014XBIB019","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"Pan S. J.","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"S021800142351014XBIB020","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jmapro.2020.01.044","volume":"63","author":"Jiao W.","year":"2021","journal-title":"J. Manuf. Process."},{"key":"S021800142351014XBIB021","doi-asserted-by":"crossref","first-page":"10844","DOI":"10.1109\/JSEN.2021.3059860","volume":"21","author":"Guo R.","year":"2021","journal-title":"IEEE Sens. J."},{"key":"S021800142351014XBIB022","doi-asserted-by":"crossref","first-page":"105008","DOI":"10.1016\/j.engappai.2022.105008","volume":"117","author":"Wu Z.","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"S021800142351014XBIB023","first-page":"10","volume":"2","author":"Ferguson M.","year":"2018","journal-title":"J. Smart Sustain Manuf. Syst."},{"key":"S021800142351014XBIB024","doi-asserted-by":"crossref","first-page":"119951","DOI":"10.1109\/ACCESS.2020.3005450","volume":"8","author":"Pan H.","year":"2020","journal-title":"IEEE Access"},{"first-page":"1717","volume-title":"Proc. IEEE Conf. Computer Vision and Pattern Recognition","author":"Oquab M.","key":"S021800142351014XBIB025"},{"first-page":"6848","volume-title":"Proc. IEEE Conf. Computer Vision and Pattern Recognition","author":"Zhang X.","key":"S021800142351014XBIB027"},{"key":"S021800142351014XBIB028","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1109\/CVPR.2017.243","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Huang G.","year":"2017"},{"key":"S021800142351014XBIB029","doi-asserted-by":"crossref","first-page":"4510","DOI":"10.1109\/CVPR.2018.00474","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Sandler M.","year":"2018"},{"key":"S021800142351014XBIB030","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/ICCV.2019.00140","volume-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Howard A.","year":"2019"},{"key":"S021800142351014XBIB031","first-page":"448","volume-title":"Int. Conf. Machine Learning, PMLR","author":"Ioffe S.","year":"2015"},{"key":"S021800142351014XBIB032","first-page":"1224","volume":"45","author":"Xue-Song Z.","year":"2019","journal-title":"Acta Autom. Sin."},{"key":"S021800142351014XBIB033","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1080\/01431161.2020.1856964","volume":"42","author":"Xia M.","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"S021800142351014XBIB034","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00170-021-06592-8","volume":"113","author":"Zheng X.","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"S021800142351014XBIB035","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"Everingham M.","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"S021800142351014XBIB036","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.ndteint.2005.01.011","volume":"38","author":"Sun Y.","year":"2005","journal-title":"NDT & E Int."}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021800142351014X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T06:52:43Z","timestamp":1695711163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S021800142351014X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":35,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.1142\/S021800142351014X"],"URL":"https:\/\/doi.org\/10.1142\/s021800142351014x","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"type":"print","value":"0218-0014"},{"type":"electronic","value":"1793-6381"}],"subject":[],"published":{"date-parts":[[2023,8]]},"article-number":"2351014"}}