{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T02:04:04Z","timestamp":1779674644182,"version":"3.53.1"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In industrial quality control, the precise identification of severe structural weld defects is paramount. Traditional 2D image-based detection methods are susceptible to illumination and texture interference, while high-precision 3D laser scanning solutions are costly and impractical for large-scale deployment. To achieve reliable geometric defect detection at low cost, this paper proposes a detection framework based on monocular depth estimation and a dual-attention point cloud network. First, YOLOv8 is employed for rapid region of interest extraction, and an advanced monocular depth estimation model generates 3D pseudo-point clouds containing geometric information. Secondly, addressing the challenge of distinct spatial orientation features in missed weld defects that are prone to confusion, this paper introduces a dual-attention-enhanced point cloud classification network named DA-PointNet++. This model embeds dual-attention modules within the PointNet++ backbone network, enhancing key feature representation in both the channel and spatial dimensions. Experimental results demonstrate that this approach achieves an accuracy of 93.67% and a recall rate of 90.51% in a unified binary classification task for general weld defect detection, effectively identifying both normal welds and complex missed weld defects. Compared to PointConv, Dynamic Graph Convolutional Neural Network (DGCNN), and mainstream Point Cloud Transformer, this method significantly reduces false negative rates while maintaining low computational costs, offering a cost-effective solution for industrial automation.<\/jats:p>","DOI":"10.3390\/s26113321","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T01:45:08Z","timestamp":1779673508000},"page":"3321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Industrial Weld Defect Detection Based on Monocular Depth Estimation and Dual-Attention Point Cloud Network"],"prefix":"10.3390","volume":"26","author":[{"given":"Nannan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang 529568, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","article-title":"A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects","volume":"285","author":"Song","year":"2013","journal-title":"Appl. Surf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., and Shao, L. (2021). Surface Defect Detection Methods for Industrial Products: A Review. Appl. Sci., 11.","DOI":"10.3390\/app11167657"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"94553","DOI":"10.1109\/ACCESS.2025.3574083","article-title":"Advances and Challenges in Deep Learning for Automated Welding Defect Detection: A Technical Survey","volume":"13","author":"Mohammed","year":"2025","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12131","DOI":"10.1007\/s10462-023-10475-7","article-title":"A Survey of Real-Time Surface Defect Inspection Methods Based on Deep Learning","volume":"56","author":"Liu","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deng, H., Cheng, Y., Feng, Y., and Xiang, J. (2021). Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed. Symmetry, 13.","DOI":"10.3390\/sym13091731"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"040801","DOI":"10.1115\/1.4049535","article-title":"Image-Based Surface Defect Detection Using Deep Learning: A Review","volume":"21","author":"Bhatt","year":"2021","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108961","DOI":"10.1016\/j.engappai.2024.108961","article-title":"Ensemble-Based Deep Learning Model for Welding Defect Detection and Classification","volume":"136","author":"Vasan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s00170-020-05882-x","article-title":"Surface Defect Detection of Aluminum Alloy Welds with 3D Depth Image and 2D Gray Image","volume":"110","author":"Yan","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3390\/s110100506","article-title":"A Laser-Based Vision System for Weld Quality Inspection","volume":"11","author":"Huang","year":"2011","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Godard, C., Aodha, O.M., Firman, M., and Brostow, G. (2019). Digging Into Self-Supervised Monocular Depth Estimation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00393"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2019). Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00864"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., and Zhao, H. (2024). Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Afshar, M.F., Shirmohammadi, Z., Ghahramani, S.A.A.G., Noorparvar, A., and Hemmatyar, A.M.A. (2023). An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems. Sustainability, 15.","DOI":"10.3390\/su15118897"},{"key":"ref_15","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","article-title":"PCT: Point Cloud Transformer","volume":"7","author":"Guo","year":"2021","journal-title":"Comput. Vis. Media"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., and Pang, R. (2019). Searching for MobileNetV3. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention Mechanisms in Computer Vision: A Survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep Learning for 3D Point Clouds: A Survey 2020","volume":"43","author":"Guo","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_23","first-page":"1","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., and Fuxin, L. (2018). PointConv: Deep Convolutional Networks on 3D Point Clouds. arXiv.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1007\/s11263-023-01790-1","article-title":"3D Object Detection for Autonomous Driving: A Comprehensive Survey","volume":"131","author":"Mao","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1007\/s11831-023-10032-z","article-title":"Review of Lightweight Deep Convolutional Neural Networks","volume":"31","author":"Chen","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s10921-017-0453-1","article-title":"3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface","volume":"36","author":"Pham","year":"2017","journal-title":"J. Nondestruct. Eval."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Fu, C.-W., and Jia, J. (2019). PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00571"},{"key":"ref_31","unstructured":"Ma, X., Qin, C., You, H., Ran, H., and Fu, Y. (2022). Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework 2022. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., and Tian, Q. (2019). Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00344"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, X., Jiang, L., Wang, P.-S., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., and Zhao, H. (2024). Point Transformer V3: Simpler, Faster, Stronger. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00463"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23192","DOI":"10.52202\/068431-1685","article-title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","volume":"35","author":"Qian","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","article-title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer","volume":"44","author":"Ranftl","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Li, Z., Gao, C., Lv, X., and Zhang, M. (2023). A review of surface defect detection for metal materials based on deep learning. Manuf. Technol. Mach. Tool., 61\u201367."},{"key":"ref_37","first-page":"0412008","article-title":"Research on defect detection method for power battery laser welding based on 3D vision","volume":"62","author":"Zhao","year":"2025","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_38","first-page":"0402105","article-title":"Intelligent online detection of laser welding defects based on high density point clouds (invited)","volume":"51","author":"Zhang","year":"2024","journal-title":"Chin. J. Lasers"},{"key":"ref_39","first-page":"298","article-title":"A lightweight weld defect identification algorithm based on convolutional neural network","volume":"42","author":"Xu","year":"2025","journal-title":"J. Anhui Univ. Technol. (Nat. Sci.)"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T01:52:36Z","timestamp":1779673956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,23]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26113321"],"URL":"https:\/\/doi.org\/10.3390\/s26113321","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,23]]}}}