{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T08:29:24Z","timestamp":1778056164801,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Liaoning Electric Supply Power Co., Ltd. Management Science and Technology Project Funding","award":["SGTYHT\/23-JS-001"],"award-info":[{"award-number":["SGTYHT\/23-JS-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network\u2019s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.<\/jats:p>","DOI":"10.3390\/s24155021","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T13:57:28Z","timestamp":1722866248000},"page":"5021","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds"],"prefix":"10.3390","volume":"24","author":[{"given":"Yunyi","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fenghua","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"National Digital Agriculture Regional Innovation Center (Northeast), Shenyang 110866, China"},{"name":"Key Laboratory of Smart Agriculture Technology in Liaoning Province, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote Sensing Methods for Power Line Corridor Surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9350","DOI":"10.1109\/TIM.2020.3031194","article-title":"A Review on State-of-the-Art Power Line Inspection Techniques","volume":"69","author":"Yang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/JSTARS.2023.3289599","article-title":"SS-IPLE: Semantic Segmentation of Electric Power Corridor Scene and Individual Power Line Extraction From UAV-Based Lidar Point Cloud","volume":"16","author":"Liu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61929","DOI":"10.1109\/ACCESS.2023.3287940","article-title":"Exploring Semantic Information Extraction From Different Data Forms in 3D Point Cloud Semantic Segmentation","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","first-page":"54","article-title":"A Survey of Semantic Segmentation of Point Cloud Based on Deep Learning","volume":"31","author":"Liu","year":"2023","journal-title":"J. Beijing Inst. Petrochem. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-View Convolutional Neural Networks for 3D Shape Recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Le, T., and Duan, Y. (2018, January 18\u201323). PointGrid: A Deep Network for 3D Shape Understanding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00959"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6807","DOI":"10.1109\/TPAMI.2021.3098789","article-title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception","volume":"44","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_10","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_11","first-page":"23192","article-title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P., and Koltun, V. (2021, January 11\u201317). Point Transformer. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lai, X., Liu, J., Jiang, L., Wang, L., Zhao, H., Liu, S., Qi, X., and Jia, J. (2022, January 18\u201324). Stratified Transformer for 3D Point Cloud Segmentation. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00831"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qiu, S., Anwar, S., and Barnes, N. (2021, January 20\u201325). Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00180"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1109\/TMM.2022.3212914","article-title":"Context-Aware 3D Point Cloud Semantic Segmentation with Plane Guidance","volume":"25","author":"Weng","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., and Pan, C. (2019, January 15\u201320). Relation-Shape Convolutional Neural Network for Point Cloud Analysis. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00910"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fan, S., Dong, Q., Zhu, F., Lv, Y., Ye, P., and Wang, F.-Y. (2021, January 20\u201325). SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01427"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shuang, F., Li, P., Li, Y., Zhang, Z., and Li, X. (2022). MSIDA-Net: Point Cloud Semantic Segmentation via Multi-Spatial Information and Dual Adaptive Blocks. Remote Sens., 14.","DOI":"10.3390\/rs14092187"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TCYB.2022.3159815","article-title":"FG-Net: A Fast and Accurate Framework for Large-Scale LiDAR Point Cloud Understanding","volume":"53","author":"Liu","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gardner, A., Kanno, J., Duncan, C.A., and Selmic, R.R. (2019, January 6\u20139). Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914200"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121128","DOI":"10.1109\/ACCESS.2021.3108630","article-title":"Zernike Pooling: Generalizing Average Pooling Using Zernike Moments","volume":"9","author":"Theodoridis","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"053031","DOI":"10.1117\/1.JEI.32.5.053031","article-title":"LPFE-Net: A Local Parallel Feature Extraction Network for Large-Scale Point Cloud Semantic Segmentation","volume":"32","author":"Ai","year":"2023","journal-title":"J. Electron. Imaging"},{"key":"ref_24","unstructured":"Girdhar, R., and Ramanan, D. (2017, January 4\u20139). Attentional Pooling for Action Recognition. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14787","DOI":"10.1007\/s10489-021-02840-2","article-title":"Multi-View Attention-Convolution Pooling Network for 3D Point Cloud Classification","volume":"52","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., RoyChowdhury, A., and Maji, S. (2015, January 7\u201313). Bilinear CNN Models for Fine-Grained Visual Recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pham, N.D., and Pagh, R. (2013, January 11\u201314). Fast and Scalable Polynomial Kernels via Explicit Feature Maps. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487591"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, S., Kim, S.H., Xiong, Y., and Kim, H.J. (2023, January 17\u201324). Self-Positioning Point-Based Transformer for Point Cloud Understanding. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02089"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/5021\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:29:21Z","timestamp":1760110161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/5021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":28,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24155021"],"URL":"https:\/\/doi.org\/10.3390\/s24155021","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,2]]}}}