{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T19:15:10Z","timestamp":1735586110569},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shaanxi Province, China","award":["2022JM\u2014033"]},{"name":"2023 Graduate Innovation Fund Project of Xi\u2032an Polytechnic University","award":["chx2023026"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The regular detection of weld seams in large-scale special equipment is crucial for improving safety and efficiency, and this can be achieved effectively through the use of weld seam tracking and detection robots. In this study, a wall-climbing robot with integrated seam tracking and detection was designed, and the wall climbing function was realized via a permanent magnet array and a Mecanum wheel. The function of weld seam tracking and detection was realized using a DeepLabv3+ semantic segmentation model. Several optimizations were implemented to enhance the deployment of the DeepLabv3+ semantic segmentation model on embedded devices. Mobilenetv2 was used to replace the feature extraction network of the original model, and the convolutional block attention module attention mechanism was introduced into the encoder module. All traditional 3\u00d73 convolutions were substituted with depthwise separable dilated convolutions. Subsequently, the welding path was fitted using the least squares method based on the segmentation results. The experimental results showed that the volume of the improved model was reduced by 92.9%, only being 21.8 Mb. The average precision reached 98.5%, surpassing the original model by 1.4%. The reasoning speed was accelerated to 21 frames\/s, satisfying the real-time requirements of industrial detection. The detection robot successfully realizes the autonomous identification and tracking of weld seams. This study remarkably contributes to the development of automatic and intelligent weld seam detection technologies.<\/jats:p>","DOI":"10.3390\/s23156725","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T06:10:45Z","timestamp":1690524645000},"page":"6725","source":"Crossref","is-referenced-by-count":4,"title":["Weld Seam Tracking and Detection Robot Based on Artificial Intelligence Technology"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5851-2397","authenticated-orcid":false,"given":"Jiuxin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Lei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}]},{"given":"Jiahui","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Man","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Yurong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Minghu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Yaoheng","family":"Su","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]},{"given":"Dingze","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2032an Polytechnic University, Xi\u2032an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","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":"ref_2","doi-asserted-by":"crossref","first-page":"114260","DOI":"10.1016\/j.oceaneng.2023.114260","article-title":"A comprehensive review of in-pipe robots","volume":"277","author":"Kahnamouei","year":"2023","journal-title":"Ocean. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s00170-007-1257-8","article-title":"Arc welding robot system with seam tracking and weld pool control based on passive vision","volume":"39","author":"Shen","year":"2008","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alkalla, M.G., Fanni, M.A., and Mohamed, A.M. (2015, January 7\u201311). A novel propeller-type climbing robot for vessels inspection. Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, Republic of Korea.","DOI":"10.1109\/AIM.2015.7222776"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5497","DOI":"10.1007\/s00170-021-08616-9","article-title":"Autonomous mobile welding robot for discontinuous weld seam recognition and tracking","volume":"119","author":"Guo","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1007\/s00170-022-10019-3","article-title":"Multiseam tracking with a portable robotic welding system in unstructured environments","volume":"122","author":"Yu","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s10846-017-0764-6","article-title":"Intelligent environment recognition and prediction for NDT inspection through autonomous climbing robot","volume":"92","author":"Teixeira","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1108\/IR-11-2016-0294","article-title":"Seam tracking investigation via striped line laser sensor","volume":"44","author":"Zou","year":"2017","journal-title":"Ind. Robot. Int. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/rob.22042","article-title":"Weld line recognition and path planning with spherical tank inspection robots","volume":"39","author":"Li","year":"2022","journal-title":"J. Field Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104273","DOI":"10.1016\/j.autcon.2022.104273","article-title":"Intelligent robotic systems for structural health monitoring: Applications and future trends","volume":"139","author":"Tian","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103907","DOI":"10.1016\/j.robot.2021.103907","article-title":"A magnetic crawler wall-climbing robot with capacity of high payload on the convex surface","volume":"148","author":"Hu","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104164","DOI":"10.1016\/j.robot.2022.104164","article-title":"Compact lightweight magnetic gripper designed for biped climbing robots based on coaxial rotation of multiple magnets","volume":"155","author":"Zhu","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.procs.2018.07.110","article-title":"Modeling and experimental analysis of suction pressure generated by active suction chamber based wall climbing robot with a novel bottom restrictor","volume":"133","author":"Navaprakash","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110306","DOI":"10.1016\/j.oceaneng.2021.110306","article-title":"Development of a new hull adsorptive underwater climbing robot using the Bernoulli negative pressure effect","volume":"243","author":"Guo","year":"2022","journal-title":"Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102889","DOI":"10.1016\/j.mechatronics.2022.102889","article-title":"A 6-DOF humanoid wall-climbing robot with flexible adsorption feet based on negative pressure suction","volume":"87","author":"Shi","year":"2022","journal-title":"Mechatronics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1007\/s00170-021-07398-4","article-title":"Research and prospect of welding monitoring technology based on machine vision","volume":"115","author":"Fan","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1109\/TIM.2009.2028222","article-title":"Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection","volume":"59","author":"Li","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1108\/01439910910950559","article-title":"Efficient weld seam detection for robotic welding based on local image processing","volume":"36","author":"Shi","year":"2009","journal-title":"Ind. Robot. Int. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s10846-015-0331-y","article-title":"Parameter Self-Optimizing Clustering for Autonomous Extraction of the Weld Seam Based on Orientation Saliency in Robotic MAG Welding","volume":"83","author":"He","year":"2016","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3739","DOI":"10.1007\/s00170-017-0380-4","article-title":"Autonomous detection and identification of weld seam path shape position","volume":"92","author":"Sulaiman","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"JAMDSM0028","DOI":"10.1299\/jamdsm.2022jamdsm0028","article-title":"Weld seam track identification for industrial robot based on illumination correction and center point extraction","volume":"16","author":"Liang","year":"2022","journal-title":"J. Adv. Mech. Des. Syst. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107827","DOI":"10.1016\/j.compag.2023.107827","article-title":"Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms","volume":"209","author":"Wu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115158","DOI":"10.1016\/j.engstruct.2022.115158","article-title":"Novel visual crack width measurement based on backbone double-scale features for improved detection automation","volume":"274","author":"Tang","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"164952","DOI":"10.1109\/ACCESS.2019.2953313","article-title":"An Automatic Detection and Identification Method of Welded Joints Based on Deep Neural Network","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3126366","article-title":"Automatic Detection and Location of Weld Beads with Deep Convolutional Neural Networks","volume":"70","author":"Yang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"22651","DOI":"10.1038\/s41598-022-27209-4","article-title":"Design and analysis of welding inspection robot","volume":"12","author":"Zhang","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, J., Li, B., Dong, L., Wang, X., and Tian, M. (2022). Weld Seam Identification and Tracking of Inspection Robot Based on Deep Learning Network. Drones, 6.","DOI":"10.3390\/drones6080216"},{"key":"ref_29","unstructured":"(2023, May 20). Available online: https:\/\/www.kaggle.com\/datasets\/engineeringubu\/fsw-aa5083-aa5061."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.Y., Cubuk, E.D., Le, Q.V., and Zoph, B. (2021, January 20\u201325). Simple copy-paste is a strong data augmentation method for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6725\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T06:41:12Z","timestamp":1690526472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,27]]},"references-count":36,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156725"],"URL":"https:\/\/doi.org\/10.3390\/s23156725","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,27]]}}}