{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:31:17Z","timestamp":1786509077955,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"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>The angular distribution of surface-strands in oriented strand board (OSB) is closely associated with board mechanical properties and mat formation quality. By acquiring surface images through vision sensing and combining them with deep learning-based segmentation, the angle classes of OSB surface-strands can be segmented and statistically analyzed automatically. However, OSB surface images contain complex strand textures, blurred boundaries, local adhesion between adjacent strands, and subtle differences among neighboring angle classes. To address these challenges, this study proposes a direction-aware dual-branch semantic segmentation network (DiBiNet) for pixel-level segmentation of surface-strand angle classes. OSB surface images were collected using a Hikrobot MV-CE120-10UC color industrial camera, and an 11-class dataset was constructed, including the background and ten angle classes from 0\u00b0 to 90\u00b0. The samples were cropped to 512 \u00d7 512 pixels, and an improved angle-semantic-consistent Copy\u2013Paste strategy was used to augment the training data. DiBiNet enhances directional feature representation through a Directional Strip Detail Enhancement Module, improves semantic feature modeling by combining MobileNetV3-Small with a DS-MobileViT Block, and fuses the two branches through a Bilateral Gated Fusion Module. Considering the continuity among angle classes, Direction Vector Auxiliary Supervision is introduced to map discrete angle labels into continuous direction vectors, thereby improving discrimination among neighboring classes. Experiments on the self-constructed dataset show that DiBiNet achieves a mean Intersection over Union (mIoU) of 0.8532, an overall pixel accuracy (Acc) of 0.8823, and a Dice coefficient of 0.8623, outperforming several representative semantic segmentation models. After 8-bit integer (INT8) + 16-bit floating-point (FP16) mixed quantization, the model achieves a neural processing unit (NPU) inference speed of 34.0 frames per second (FPS) on the RK3588 platform, demonstrating its potential for vision-based sensing and edge AI inspection.<\/jats:p>","DOI":"10.3390\/s26165055","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:24:53Z","timestamp":1786353893000},"page":"5055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Direction-Aware Dual-Branch Network for Surface-Strand Orientation Segmentation of Oriented Strand Board"],"prefix":"10.3390","volume":"26","author":[{"given":"Changyu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8473-8990","authenticated-orcid":false,"given":"Yin","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s002260100118","article-title":"The relationship between the arrangement of wood strands at the surface of OSB and the modulus of rupture determined by image analysis","volume":"35","author":"Nishimura","year":"2001","journal-title":"Wood Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s002260100117","article-title":"Evaluation of the arrangement of wood strands at the surface of OSB by image analysis","volume":"36","author":"Nishimura","year":"2002","journal-title":"Wood Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s00226-007-0167-0","article-title":"Effect of mat structure on modulus of elasticity of oriented strandboard","volume":"42","author":"Chen","year":"2008","journal-title":"Wood Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lunguleasa, A., Dumitrascu, A.-E., Spirchez, C., and Ciobanu, V.-D. (2021). Influence of the strand characteristics on the properties of oriented strand boards obtained from resinous and broad-leaved fast-growing species. Appl. Sci., 11.","DOI":"10.3390\/app11041784"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018). BiSeNet: Bilateral segmentation network for real-time semantic segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"BiSeNet V2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s00226-024-01619-1","article-title":"Real-time tracking of the characteristics of strands in OSB production lines","volume":"59","author":"Hong","year":"2025","journal-title":"Wood Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Z., Lin, Y., Chen, D., Ai, Z., and Zhang, H. (2024). A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation. Sensors, 24.","DOI":"10.3390\/s24051635"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mou, X., Chen, H., Yu, X., Chen, L., Peng, Z., and Wang, R. (2024). Lightweight Segmentation Method for Wood Panel Images Based on Improved DeepLabV3+. Electronics, 13.","DOI":"10.3390\/electronics13234658"},{"key":"ref_13","first-page":"5400714","article-title":"DWin-HRFormer: A high-resolution Transformer model with directional windows for semantic segmentation of urban construction land","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4106218","DOI":"10.1109\/TGRS.2026.3685508","article-title":"DSCH-Net: Diffusion-State-Contextual Hybrid Network for Physics-Inspired and Direction-Aware Dehazing of Remote Sensing Imagery","volume":"64","author":"Sultan","year":"2026","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations, Online."},{"key":"ref_17","unstructured":"Mehta, S., and Rastegari, M. (2022, January 25\u201329). MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer. Proceedings of the International Conference on Learning Representations, Online."},{"key":"ref_18","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_19","unstructured":"Wada, K. (2026, July 12). labelme: Image Polygonal Annotation with Python. Zenodo. Available online: https:\/\/zenodo.org\/records\/5711226."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T., and Cubuk, E.D. (2021, January 20\u201325). Simple copy-paste is a strong data augmentation method for instance segmentation. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"ref_21","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision, IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_23","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_28","first-page":"166","article-title":"LSTFormer: Lightweight Semantic Segmentation Network Based on Swin Transformer","volume":"59","author":"Yang","year":"2023","journal-title":"J. Comput. Eng. Appl."},{"key":"ref_29","unstructured":"(2026, July 06). Rockchip rknn-toolkit2: RKNN-Toolkit2 and RKNN-Toolkit-Lite2 Software Development Kit. Available online: https:\/\/github.com\/airockchip\/rknn-toolkit2."},{"key":"ref_30","unstructured":"Rockchip Electronics Co., Ltd. (2026, July 06). RK3588 Datasheet V1.6. Available online: https:\/\/wiki.friendlyelec.com\/wiki\/images\/e\/ee\/Rockchip_RK3588_Datasheet_V1.6-20231016.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:14:58Z","timestamp":1786508098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,9]]},"references-count":30,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165055"],"URL":"https:\/\/doi.org\/10.3390\/s26165055","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,9]]}}}