乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:49:36Z","timestamp":1740149376384,"version":"3.37.3"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"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":["61772185"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.<\/jats:p>","DOI":"10.3390\/s20082212","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T08:01:46Z","timestamp":1586937706000},"page":"2212","source":"Crossref","is-referenced-by-count":30,"title":["LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8907-3139","authenticated-orcid":false,"given":"Shuai","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1323-3175","authenticated-orcid":false,"given":"Cheng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4355-1737","authenticated-orcid":false,"given":"Lipei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4807-3703","authenticated-orcid":false,"given":"Siqi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4330-240X","authenticated-orcid":false,"given":"Xiaohan","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, Z., Jin, C., Jin, W., Lee, J., Zhang, Z., Peng, C., and Xu, G. (2018, January 11\u201313). Industrial AI Enabled Prognostics for High-speed Railway Systems. Proceedings of the 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA.","DOI":"10.1109\/ICPHM.2018.8448431"},{"key":"ref_2","first-page":"140","article-title":"Study on Stagger Value and Steady Arm Slope of High Speed Railway Catenary","volume":"60","author":"Luo","year":"2016","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, S., Xu, C., Chen, L., and Liu, Z. (2018, January 6\u20138). Speed Regulation of Overhead Catenary System Inspection Robot for High-Speed Railway through Reinforcement Learning. Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Guangzhou, China.","DOI":"10.1109\/SmartWorld.2018.00239"},{"key":"#cr-split#-ref_4.1","doi-asserted-by":"crossref","unstructured":"Tu, X., Xu, C., Liu, S., Xie, G., and Li, R. Real-Time Depth Estimation with an Optimized Encoder-Decoder Architecture on Embedded Devices. Proceedings of the 2019 IEEE 21st International Conference on High Performance Computing and Communications, Zhangjiajie, China, 10-12 August 2019","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00296"},{"key":"#cr-split#-ref_4.2","unstructured":"In Proceedings of the IEEE 17th International Conference on Smart City, Zhangjiajie, China, 10-12 August 2019"},{"key":"#cr-split#-ref_4.3","unstructured":"In Proceedings of the IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), Zhangjiajie, China, 10-12 August 2019."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"135678","DOI":"10.1109\/ACCESS.2019.2942079","article-title":"An Efficient Method for High-Speed Railway Dropper Fault Detection Based on Depthwise Separable Convolution","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","unstructured":"Tan, P., Li, X., Wu, Z., Ding, J., Ma, J., Chen, Y., Fang, Y., and Ning, Y. (2019). Multialgorithm Fusion Image Processing for High Speed Railway Dropper Failure-Defect Detection. IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173366","DOI":"10.1109\/ACCESS.2019.2955753","article-title":"Contact Wire Support Defect Detection Using Deep Bayesian Segmentation Neural Networks and Prior Geometric Knowledge","volume":"7","author":"Kang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"239","article-title":"Catenary geometric parameters detection method based on 3D point cloud","volume":"39","author":"Jingsong","year":"2018","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_9","first-page":"57","article-title":"Catenary Geometry Parameter Detection Method Based on Laser Scanning","volume":"24","author":"Dong","year":"2016","journal-title":"Comput. Meas. Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"179118","DOI":"10.1109\/ACCESS.2019.2958671","article-title":"A Review of Deep Learning-Based Semantic Segmentation for Point Cloud","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xie, Y., Tian, J., and Zhu, X. (2020). Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation. IEEE Geosci. Remote Sens. Mag.","DOI":"10.1109\/MGRS.2019.2937630"},{"key":"ref_12","first-page":"2103","article-title":"3D point cloud segmentation, classification and recognition algorithm of railway scene","volume":"38","author":"Guo","year":"2017","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, J., Han, Z., and Wang, L. (2018, January 27\u201329). A Steady Arm Slope Detection Method Based on 3D Point Cloud Segmentation. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.","DOI":"10.1109\/ICIVC.2018.8492785"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, Z., Yang, C., and Liu, Z. (2019). Cantilever Structure Segmentation and Parameters Detection Based on Concavity and Convexity of 3D Point Clouds. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2019.2930158"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pastucha, E. (2016). Catenary system detection, localization and classification using mobile scanning data. Remote Sens., 8.","DOI":"10.3390\/rs8100801"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jung, J., Chen, L., Sohn, G., Luo, C., and Won, J.U. (2016). Multi-range conditional random field for classifying railway electrification system objects using mobile laser scanning data. Remote Sens., 8.","DOI":"10.20944\/preprints201609.0088.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1109\/JSTARS.2019.2918272","article-title":"Multi-Scale Hierarchical CRF for Railway Electrification Asset Classification From Mobile Laser Scanning Data","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhong, J., Lyu, Y., Liu, K., Han, Y., Wang, L., and Liu, W. (2018, January 14\u201317). Location and fault detection of catenary support components based on deep learning. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.","DOI":"10.1109\/I2MTC.2018.8409637"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, Z., Wang, H., and Liu, K. (2017, January 18\u201320). High-speed railway catenary components detection using the cascaded convolutional neural networks. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261459"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1109\/TIM.2018.2868490","article-title":"Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning","volume":"68","author":"Kang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Guerry, J., Boulch, A., Le Saux, B., Moras, J., Plyer, A., and Filliat, D. (2017, January 22\u201329). SnapNet-R: Consistent 3D Multi-view Semantic Labeling for Robotics. Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.85"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3D Convolutional Neural Network for real-time object recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Verdoja, F., Thomas, D., and Sugimoto, A. (2017, January 10\u201314). Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019382"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Balado Frias, J., Mart\u00ednez-S\u00e1nchez, J., and Novo, A. (2019). Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data. Sensors, 19.","DOI":"10.3390\/s19163466"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zegaoui, Y., Chaumont, M., Subsol, G., Borianne, P., and Derras, M. (2019, January 22\u201324). Urban object classification with 3D Deep-Learning. Proceedings of the 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France.","DOI":"10.1109\/JURSE.2019.8809043"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"445","DOI":"10.5194\/isprs-annals-IV-2-W5-445-2019","article-title":"PointNet for the Automatic Classification of Aerial Point Clouds","volume":"IV-2\/W5","author":"Lindenbergh","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S. (2016, January 27\u201330). 3D Semantic Parsing of Large-Scale Indoor Spaces. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.170"},{"key":"ref_30","unstructured":"Yi, L., Shao, L., Savva, M., Huang, H., Zhou, Y., Wang, Q., Graham, B., Engelcke, M., Klokov, R., and Lempitsky, V. (2017). Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T18:00:07Z","timestamp":1719511207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,14]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20082212"],"URL":"https:\/\/doi.org\/10.3390\/s20082212","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,4,14]]}}}