{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:31:19Z","timestamp":1768447879139,"version":"3.49.0"},"reference-count":16,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2015,6,15]],"date-time":"2015-06-15T00:00:00Z","timestamp":1434326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,6,15]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The main purpose of this paper is to develop a method to recognize the initial welding position for large-diameter pipeline automatically, and introduce the image processing based on pulse-coupled neural network (PCNN) which is adopted by the proposed method. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 In this paper, a passive vision sensor is designed to capture weld seam images in real time. The proposed method contains two steps. The first step is to detect the rough position of the weld seam, and the second step is to recognize one of the solder joints from the local image and extract its centroid, which is regarded as the initial welding position. In each step, image segmentation and removal of small false regions based on PCNN are adopted to obtain the object regions; then, the traditional image processing theory is used for the subsequent processing. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The experimental results show the feasibility and real time of the proposed method. Based on vision sensing technology and PCNN, it is able to achieve the autonomous recognition of initial welding position in large-diameter pipeline welding. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 The proposed method can greatly shorten the time of positioning the initial welding position and satisfy the automatic welding for large-diameter pipeline. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 In the proposed method, the image pre-processing is based on PCNN, which is more robust and flexible in the complex welding environment. After that, traditional image processing theory is adopted for the subsequent processing, of which the processing speed is faster.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ir-01-2015-0011","type":"journal-article","created":{"date-parts":[[2015,6,12]],"date-time":"2015-06-12T05:22:52Z","timestamp":1434086572000},"page":"339-346","source":"Crossref","is-referenced-by-count":12,"title":["Recognition of initial welding position for large diameter pipeline based on pulse coupled neural network"],"prefix":"10.1108","volume":"42","author":[{"given":"Li Juan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Pei Huang","family":"Lou","sequence":"additional","affiliation":[]},{"given":"Xiao Ming","family":"Qian","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020122323045144800_b1","doi-asserted-by":"crossref","unstructured":"Baskoro, A.\n , \n Kabutomori, M.\n and \n Suga, Y.\n (2008), \u201cMonitoring of backside image of molten pool during aluminum pipe welding using vision sensor\u201d, \n Materials Science Forum\n , Vol. 580, pp. 379-382.","DOI":"10.4028\/www.scientific.net\/MSF.580-582.379"},{"key":"key2020122323045144800_b2","unstructured":"Cao, Y.L.\n , \n Huang, F.X.\n and \n Wang, Q.\n (2012), \u201cResearch progress and development direction of long distance pipeline welding technology in China\u201d, paper presented at Oil Forum, Vol. 1, pp. 7-11."},{"key":"key2020122323045144800_b3","doi-asserted-by":"crossref","unstructured":"Chen, X.Z.\n and \n Chen, S.B.\n (2010), \u201cThe autonomous detection and guiding of start welding position for arc welding robot\u201d, \n Industrial Robot\n , Vol. 37 No. 1, pp. 70-78.","DOI":"10.1108\/01439911011009975"},{"key":"key2020122323045144800_b4","doi-asserted-by":"crossref","unstructured":"Gao, C.\n , \n Zhou, D.\n and \n Guo, Y.\n (2013), \u201cAn iterative thresholding segmentation model using a modified pulse coupled neural network\u201d, \n Neural Processing Letters\n , Vol. 39 No. 1, pp. 81-95.","DOI":"10.1007\/s11063-013-9291-z"},{"key":"key2020122323045144800_b5","unstructured":"Gu, X.D.\n (2004), \u201cEquivalence relation between PCNN and mathematical morphology in image processing\u201d, \n Journal of Computer Aided Design & Computer Graphics\n , Vol. 16 No. 8, pp. 1029-1032."},{"key":"key2020122323045144800_b6","unstructured":"Kang, M.G.\n , \n Kim, J.H.\n , \n Park, Y.J.\n and \n Woo, G.J.\n (2007), \u201cLaser vision system for automatic seam tracking of stainless steel pipe welding machine (ICCAS 2007)\u201d, International Conference on Control, Automation and Systems, Seoul, 17-20 October, pp. 1046-1051."},{"key":"key2020122323045144800_b7","doi-asserted-by":"crossref","unstructured":"Lang, J.\n and \n Hao, Z.\n (2014), \u201cNovel image fusion method based on adaptive pulse coupled neural network and discrete multi-parameter fractional random transform\u201d, \n Optics and Lasers in Engineering\n , Vol. 52, pp. 91-98.","DOI":"10.1016\/j.optlaseng.2013.07.005"},{"key":"key2020122323045144800_b8","unstructured":"Ma, Y.D.\n , \n Liu, Q.\n and \n Qian, Z.B.\n (2004), \u201cAutomated image segmentation using improved PCNN model based on cross-entropy\u201d, \n Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, 20-22 October\n , pp. 743-746."},{"key":"key2020122323045144800_b9","doi-asserted-by":"crossref","unstructured":"Subashini, M.M.\n and \n Sahoo, S.\n (2014), \u201cPulse coupled neural networks and its applications\u201d, \n Expert Systems with Applications\n , Vol. 41 No. 8, pp. 3965-3974.","DOI":"10.1016\/j.eswa.2013.12.027"},{"key":"key2020122323045144800_b10","doi-asserted-by":"crossref","unstructured":"Shi, F.\n , \n Lin, T.\n and \n Chen, S.\n (2009), \u201cEfficient weld seam detection for robotic welding based on local image processing\u201d, \n Industrial Robot\n , Vol. 36 No. 3, pp. 277-283.","DOI":"10.1108\/01439910910950559"},{"key":"key2020122323045144800_b11","doi-asserted-by":"crossref","unstructured":"Wei, S.\n , \n Wang, J.\n , \n Lin, T.\n and \n Chen, S.\n (2012), \u201cApplication of image morphology in detecting and extracting the initial welding position\u201d, \n Journal of Shanghai Jiaotong University (Science\n ), Vol. 17 No. 3, pp. 323-326.","DOI":"10.1007\/s12204-012-1278-9"},{"key":"key2020122323045144800_b12","unstructured":"Xu, L.\n , \n Cao, M.Y.\n , \n Wang, H.X.\n and \n Michael, C.\n (2008), \u201cA method to locate initial welding position of container reinforcing plates using structured-light\u201d, Proceedings of the 27th Chinese Control Conference, Kunming, Yunnan, 16-18 July, pp. 310-314."},{"key":"key2020122323045144800_b13","unstructured":"Xu, P.\n , \n Xu, G.\n , \n Tang, X.\n and \n Yao, S.\n (2007), \u201cA visual seam tracking system for robotic arc welding\u201d, \n The International Journal of Advanced Manufacturing Technology\n , Vol. 37 Nos 1\/2, pp. 70-75."},{"key":"key2020122323045144800_b14","doi-asserted-by":"crossref","unstructured":"Yan, Z.\n and \n Li, Y.\n (2008), \n A Visual Servoing System for the Torch Alignment to Initial Welding Position\n , Springer, Berlin.","DOI":"10.1007\/978-3-540-88518-4_75"},{"key":"key2020122323045144800_b15","doi-asserted-by":"crossref","unstructured":"Ye, Z.\n , \n Wu, Y.\n , \n Wan, H.\n and \n Cao, Z.\n (2013), \u201cSegmentation of welding defect image based on exponential cross entropy and improved PCNN\u201d, \n Advanced Materials Research\n , Vol. 760, pp. 1467-1471.","DOI":"10.4028\/www.scientific.net\/AMR.760-762.1467"},{"key":"key2020122323045144800_b16","doi-asserted-by":"crossref","unstructured":"Zou, B.\n , \n Zhou, H.\n , \n Chen, Z.\n , \n Chen, H.\n and \n Xin, G.\n (2012), \u201cPCNN based welding seam image segmentation algorithm\u201d, \n Applied Mechanics and Materials\n , Vol. 155, pp. 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