{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:39:02Z","timestamp":1784309942168,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2020CXGC010206"],"award-info":[{"award-number":["2020CXGC010206"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network\u2019s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model\u2019s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model\u2019s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.<\/jats:p>","DOI":"10.3390\/s23125640","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T08:56:01Z","timestamp":1686905761000},"page":"5640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8125-6218","authenticated-orcid":false,"given":"Ang","family":"Gao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3021-9308","authenticated-orcid":false,"given":"Zhuoxuan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoyue","family":"Le","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3463-2888","authenticated-orcid":false,"given":"Dongting","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Liquid-Solid Structural Evolution and Processing of Materials, Shandong University, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9781-9677","authenticated-orcid":false,"given":"Fuxin","family":"Du","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China"},{"name":"Engineering Research Center of Intelligent Unmanned System, Ministry of Education, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2240053","DOI":"10.1142\/S0217979222400537","article-title":"Effect of arc voltage on process stability of bypass-coupling twin-wire indirect arc welding","volume":"36","author":"Wu","year":"2022","journal-title":"Int. J. Mod. Phys. B"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s00170-020-05524-2","article-title":"Advances techniques of the structured light sensing in intelligent welding robots: A review","volume":"110","author":"Yang","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1007\/s00170-021-08343-1","article-title":"Microstructure and corrosion resistance of stainless steel produced by bypass coupling twin-wire indirect arc additive manufacturing","volume":"119","author":"Wu","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"182415","DOI":"10.1109\/ACCESS.2019.2944884","article-title":"A novel 3D seam extraction method based on multi-functional sensor for V-Type weld seam","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.sna.2019.04.015","article-title":"Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing","volume":"292","author":"Wenji","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, G., Hong, Y., Gao, J., Hong, B., and Li, X. (2020). Welding seam trajectory recognition for automated skip welding guidance of a spatially intermittent welding seam based on laser vision sensor. Sensors, 20.","DOI":"10.3390\/s20133657"},{"key":"ref_7","first-page":"1","article-title":"Efficient and accurate start point guiding and seam tracking method for curve weld based on structure light","volume":"70","author":"Ma","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.optlastec.2018.01.010","article-title":"Real-time seam tracking control system based on line laser visions","volume":"103","author":"Zou","year":"2018","journal-title":"Opt. Laser Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, Y., Wang, H., Liu, Y., Zhang, B., and Hu, S. (2022). Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding. Sensors, 22.","DOI":"10.2139\/ssrn.4159894"},{"key":"ref_10","first-page":"1","article-title":"Automatic quality control of aluminium parts welds based on 3D data and artificial intelligence","volume":"2023","author":"Cardellicchio","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"ref_11","first-page":"1","article-title":"In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding","volume":"2022","author":"Baek","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101821","DOI":"10.1016\/j.rcim.2019.101821","article-title":"A robust weld seam recognition method under heavy noise based on structured-light vision","volume":"61","author":"Wang","year":"2020","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012161","DOI":"10.1088\/1742-6596\/1074\/1\/012161","article-title":"Feature point extraction based on contour detection and corner detection for weld seam","volume":"1074","author":"Zeng","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.measurement.2018.06.020","article-title":"A seam tracking system based on a laser vision sensor","volume":"127","author":"Zou","year":"2018","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rcim.2015.04.005","article-title":"Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model","volume":"37","author":"He","year":"2016","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/TII.2020.2977121","article-title":"Seam feature point acquisition based on efficient convolution operator and particle filter in GMAW","volume":"17","author":"Fan","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1007\/s00170-018-3115-2","article-title":"Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding","volume":"101","author":"Du","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111533","DOI":"10.1016\/j.sna.2019.111533","article-title":"An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding","volume":"297","author":"Xiao","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1007\/s00170-020-05964-w","article-title":"A weld line detection robot based on structure light for automatic NDT","volume":"111","author":"Dong","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108372","DOI":"10.1016\/j.ymssp.2021.108372","article-title":"Robotic seam tracking system combining convolution filter and deep reinforcement learning","volume":"165","author":"Zou","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1007\/s00170-020-06246-1","article-title":"Autonomous seam recognition and feature extraction for multi-pass welding based on laser stripe edge guidance network","volume":"111","author":"Wu","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106140","DOI":"10.1016\/j.optlaseng.2020.106140","article-title":"Conditional generative adversarial network-based training image inpainting for laser vision seam tracking","volume":"134","author":"Zou","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6098","DOI":"10.1109\/JSEN.2022.3147489","article-title":"Image denoising of seam images with deep learning for laser vision seam tracking","volume":"22","author":"Yang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lu, J., Yang, A., Chen, X., Xu, X., Lv, R., and Zhao, Z. (2022). A Seam Tracking Method Based on an Image Segmentation Deep Convolutional Neural Network. Metals, 12.","DOI":"10.3390\/met12081365"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104698","DOI":"10.1016\/j.autcon.2022.104698","article-title":"Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks","volume":"146","author":"Liu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","article-title":"A detection algorithm for cherry fruits based on the improved YOLO-v4 model","volume":"35","author":"Gai","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sha, J., Wang, J., Hu, H., Ye, Y., and Xu, G. (2023). Development of an Accurate and Automated Quality Inspection System for Solder Joints on Aviation Plugs Using Fine-Tuned YOLOv5 Models. Appl. Sci., 13.","DOI":"10.3390\/app13095290"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, W., Liu, H., Li, L., Long, Y., Wang, X., Wang, Z., Li, J., and Chang, Y. (2021). Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image. PloS ONE, 16.","DOI":"10.1371\/journal.pone.0259283"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., and Li, X. (2021). A real-time detection algorithm for Kiwifruit defects based on YOLOv5. Electronics, 10.","DOI":"10.3390\/electronics10141711"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_31","first-page":"2","article-title":"ultralytics\/yolov5: V6. 1-TensorRT TensorFlow edge TPU and OpenVINO export and inference","volume":"2","author":"Jocher","year":"2022","journal-title":"Zenodo"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_34","unstructured":"Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-based attention module. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_37","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Song, G., Liu, Y., and Wang, X. (2020, January 14\u201319). Revisiting the sibling head in object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"ref_40","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_43","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_44","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_45","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5640\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:28Z","timestamp":1760126188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125640"],"URL":"https:\/\/doi.org\/10.3390\/s23125640","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,16]]}}}