{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:15:41Z","timestamp":1777389341157,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973085"],"award-info":[{"award-number":["61973085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.<\/jats:p>","DOI":"10.3390\/s24051635","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T04:36:21Z","timestamp":1709526981000},"page":"1635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation"],"prefix":"10.3390","volume":"24","author":[{"given":"Yuhang","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhezhuang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijie","family":"Ai","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compag.2017.03.015","article-title":"Review of the use of air-coupled ultrasonic technologies for nondestructive testing of wood and wood products","volume":"137","author":"Fang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ndteint.2017.07.014","article-title":"Applied multiresolution analysis to infrared images for defects detection in materials","volume":"92","author":"Kabouri","year":"2017","journal-title":"NDT E Int."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Du, X., Li, J., Feng, H., and Chen, S. (2018). Image reconstruction of internal defects in wood based on segmented propagation rays of stress waves. Appl. Sci., 8.","DOI":"10.3390\/app8101778"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.compag.2018.11.017","article-title":"An innovative tomographic technique integrated with acoustic-laser approach for detecting defects in tree trunk","volume":"156","author":"Qiu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2501911","DOI":"10.1109\/TIM.2020.3024431","article-title":"An accurate and real-time surface defects detection method for sawn lumber","volume":"70","author":"Tu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"046101","DOI":"10.1088\/1361-6501\/ad15de","article-title":"Surface defect detection of sawn timbers based on efficient multilevel feature integration","volume":"35","author":"Zhu","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/JAS.2023.123357","article-title":"Wood Crack Detection Based on Data-Driven Semantic Segmentation Network","volume":"10","author":"Lin","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_9","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\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0168-1699(03)00049-8","article-title":"Image segmentation algorithms applied to wood defect detection","volume":"41","author":"Funck","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s11676-019-00925-w","article-title":"An improved binarization algorithm of wood image defect segmentation based on non-uniform background","volume":"30","author":"Luo","year":"2019","journal-title":"J. For. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/j.ijleo.2013.07.098","article-title":"The segmentation of timber defects based on color and the mathematical morphology","volume":"125","author":"Chen","year":"2014","journal-title":"Optik"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mahram, A., Shayesteh, M.G., and Jafarpour, S. (2012, January 3\u20134). Classification of wood surface defects with hybrid usage of statistical and textural features. Proceedings of the International Conference on Telecommunications and Signal Processing, Prague, Czech Republic.","DOI":"10.1109\/TSP.2012.6256397"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"145829","DOI":"10.1109\/ACCESS.2019.2945355","article-title":"Wood defect classification based on two-dimensional histogram constituted by LBP and local binary differential excitation pattern","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.ijleo.2015.05.101","article-title":"Study on the identification of the wood surface defects based on texture features","volume":"126","author":"Xie","year":"2015","journal-title":"Optik-Int. J. Light Electron Opt."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s00226-009-0287-9","article-title":"Wood defect classification based on image analysis and support vector machines","volume":"44","author":"Gu","year":"2010","journal-title":"Wood Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11676-017-0572-7","article-title":"A novel image segmentation approach for wood plate surface defect classification through convex optimization","volume":"29","author":"Chang","year":"2018","journal-title":"J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1007\/s11676-018-00874-w","article-title":"Recognition of wood surface defects with near infrared spectroscopy and machine vision","volume":"30","author":"Yu","year":"2019","journal-title":"J. For. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"123453","DOI":"10.1109\/ACCESS.2019.2937461","article-title":"A fully convolutional neural network for wood defect location and identification","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107357","DOI":"10.1016\/j.measurement.2019.107357","article-title":"Application of deep convolutional neural network on feature extraction and detection of wood defects","volume":"152","author":"He","year":"2020","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111937","DOI":"10.1016\/j.measurement.2022.111937","article-title":"Detection method of timber defects based on target detection algorithm","volume":"203","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Meng, W., and Yuan, Y. (2023). SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network. Sensors, 23.","DOI":"10.3390\/s23218705"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ge, Y., Jiang, D., and Sun, L. (2023). Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net. Sensors, 23.","DOI":"10.3390\/s23104837"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"122789","DOI":"10.1016\/j.eswa.2023.122789","article-title":"Wood broken defect detection with laser profilometer based on Bi-LSTM network","volume":"242","author":"Xu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108338","DOI":"10.1016\/j.knosys.2022.108338","article-title":"A nondestructive automatic defect detection method with pixelwise segmentation","volume":"242","author":"Yang","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2520912","DOI":"10.1109\/TIM.2022.3218547","article-title":"Joint Attention-Guided feature fusion network for saliency detection of surface defects","volume":"71","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9709","DOI":"10.1109\/TIM.2020.3002277","article-title":"EDRNet: Encoder\u2013decoder residual network for salient object detection of strip steel surface defects","volume":"69","author":"Song","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","first-page":"5004914","article-title":"Dense attention-guided cascaded network for salient object detection of strip steel surface defects","volume":"71","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106876","DOI":"10.1016\/j.engappai.2023.106876","article-title":"DeepCrackAT: An effective crack segmentation framework based on learning multi-scale crack features","volume":"126","author":"Lin","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/JSEN.2022.3229031","article-title":"SDDet: An Enhanced Encoder\u2013Decoder Network With Hierarchical Supervision for Surface Defect Detection","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112177","DOI":"10.1016\/j.measurement.2022.112177","article-title":"RFIENet: RGB-thermal feature interactive enhancement network for semantic segmentation of insulator in backlight scenes","volume":"205","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_34","first-page":"3505515","article-title":"A Lightweight Network for Defect Detection in Nickel-Plated Punched Steel Strip Images","volume":"72","author":"Liang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20910","DOI":"10.1109\/JSEN.2022.3208580","article-title":"A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00170-022-10335-8","article-title":"A New Lightweight Deep Neural Network for Surface Scratch Detection","volume":"123","author":"Li","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_37","unstructured":"Yu, F., and Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding convolution for semantic segmentation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the Fourth International Conference on 3D Vision, Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_41","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"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, L., 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, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_43","unstructured":"Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. (2021). Advances in Neural Information Processing Systems, NeurIPS."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1635\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:15Z","timestamp":1760105295000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,2]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051635"],"URL":"https:\/\/doi.org\/10.3390\/s24051635","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,2]]}}}