{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:15:17Z","timestamp":1740154517947,"version":"3.37.3"},"reference-count":28,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"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","61932010"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"As vital infrastructures, high-speed railways support the development of transportation. To maintain the punctuality and safety of railway systems, researchers have employed manual and computer vision methods to monitor overhead contact systems (OCSs), but they have low efficiency. Investigators have also used light detection and ranging (LiDAR) to generate point clouds by emitting laser beams. The point cloud is segmented for automatic OCS recognition, which improves recognition efficiency. However, existing LiDAR point cloud segmentation methods have high computational\/model complexity and latency. In addition, they cannot adapt to embedded devices with different architectures. To overcome these issues, this article presents a lightweight neural network EffNet consisting of three modules: ExtractA, AttenA, and AttenB. ExtractA extracts the features from the disordered and irregular point clouds of an OCS. AttenA keeps information flowing in EffNet while extracting useful features. AttenB uses channel and spatialwise statistics to enhance important features and suppress unimportant ones efficiently. To further speed up EffNet and match it with diverse architectures, we optimized it with a generation framework of tensor programs and deployed it on embedded systems with different architectures. Extensive experiments demonstrated that EffNet has at least a 0.57% higher mean accuracy, but with 25.00% and 9.30% lower computational and model complexity for OCS recognition than others, respectively. The optimized EffNet can be adapted to different architectures. Its latency decreased by 51.97%, 56.47%, 63.63%, 82.58%, 85.85%, and 91.97% on the NVIDIA Nano CPU, TX2 CPU, UP Board CPU, Nano GPU, TX2 GPU, and RTX 2,080 Ti GPU, respectively.<\/jats:p>","DOI":"10.3390\/rs13204110","type":"journal-article","created":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T03:02:16Z","timestamp":1634266936000},"page":"4110","source":"Crossref","is-referenced-by-count":4,"title":["An Optimized Deep Neural Network for Overhead Contact System Recognition from LiDAR Point Clouds"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0019-5154","authenticated-orcid":false,"given":"Siping","family":"Liu","sequence":"first","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":"Department of Image and Network Investigation, Railway Police College, Zhengzhou 450053, 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\/0000-0002-8907-3139","authenticated-orcid":false,"given":"Shuai","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4573-7375","authenticated-orcid":false,"given":"Renfa","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, L., Song, W., Sung, Y., and Woo, S. (2021). DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud. Remote Sens., 13.","DOI":"10.3390\/rs13010066"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C., Malinverni, E.S., Frontoni, E., and Lingua, A.M. (2020). Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage. Remote Sens., 12.","DOI":"10.3390\/rs12061005"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14916","DOI":"10.3390\/rs71114916","article-title":"Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data","volume":"7","author":"Arastounia","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.measurement.2018.08.026","article-title":"Binocular vision measurement and its application in full-field convex deformation of concrete-filled steel tubular columns","volume":"130","author":"Tang","year":"2018","journal-title":"Measurement"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Tu, X., Xu, C., Liu, S., Xie, G., and Li, R. (2019, January 10\u201312). Real-Time Depth Estimation with an Optimized Encoder-Decoder Architecture on Embedded Devices. Proceedings of the IEEE 21st International Conference on High Performance Computing and Communications, Zhangjiajie, China.","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00296"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/TITS.2019.2900385","article-title":"Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology","volume":"21","author":"Wei","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1109\/TIM.2019.2930158","article-title":"Cantilever Structure Segmentation and Parameters Detection Based on Concavity and Convexity of 3-D Point Clouds","volume":"69","author":"Han","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.1109\/TII.2020.3020583","article-title":"Efficient Monocular Depth Estimation for Edge Devices in Internet of Things","volume":"17","author":"Tu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, G., Xu, Y., Pan, P., and Xing, Y. (2021). PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030472"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.3390\/s20082212","article-title":"LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning","volume":"20","author":"Lin","year":"2020","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Q.L., and Yang, Y.B. (2021, January 6\u201311). SA-Net: Shuffle Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_18","first-page":"6000","article-title":"Attention is All you Need","volume":"Volume 30","author":"Vaswani","year":"2017","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems"},{"key":"ref_19","unstructured":"Zheng, L., Jia, C., Sun, M., Wu, Z., Yu, C.H., Haj-Ali, A., Wang, Y., Yang, J., Zhuo, D., and Sen, K. (2020, January 4\u20136). Ansor: Generating High-Performance Tensor Programs for Deep Learning. Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, Banff, AB, Canada."},{"key":"ref_20","unstructured":"Lattner, C., and Adve, V. (2004, January 20\u201324). LLVM: A compilation framework for lifelong program analysis & transformation. Proceedings of the International Symposium on Code Generation and Optimization, Palo Alto, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MM.2008.57","article-title":"Parallel Computing Experiences with CUDA","volume":"28","author":"Garland","year":"2008","journal-title":"IEEE Micro"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vikhar, P.A. (2016, January 22\u201324). Evolutionary algorithms: A critical review and its future prospects. Proceedings of the International Conference on Global Trends in Signal Processing, Information Computing and Communication, Jalgaon, India.","DOI":"10.1109\/ICGTSPICC.2016.7955308"},{"key":"ref_23","unstructured":"Chen, T., Zheng, L., Yan, E., Jiang, Z., Moreau, T., Ceze, L., Guestrin, C., and Krishnamurthy, A. (2018, January 3\u20138). Learning to Optimize Tensor Programs. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Tu, X., Xu, C., Liu, S., Lin, S., Chen, L., Xie, G., and Li, R. (2020). LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection. Sensors, 20.","DOI":"10.3390\/s20216387"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018, January 8\u201314). Group Normalization. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.optlaseng.2019.06.011","article-title":"High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm","volume":"122","author":"Chen","year":"2019","journal-title":"Opt. Lasers Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T06:42:15Z","timestamp":1721457735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":28,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204110"],"URL":"https:\/\/doi.org\/10.3390\/rs13204110","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}