{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:38:17Z","timestamp":1781109497347,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T00:00:00Z","timestamp":1631923200000},"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 detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest\u2014ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24\/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.<\/jats:p>","DOI":"10.3390\/s21186281","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T22:35:20Z","timestamp":1632263720000},"page":"6281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-2848","authenticated-orcid":false,"given":"Teresa","family":"Pamu\u0142a","sequence":"first","affiliation":[{"name":"Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasi\u0144skiego 8, 40-019 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9792-6528","authenticated-orcid":false,"given":"Wies\u0142aw","family":"Pamu\u0142a","sequence":"additional","affiliation":[{"name":"Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasi\u0144skiego 8, 40-019 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105207","DOI":"10.1016\/j.ssci.2021.105207","article-title":"What factors influence risk at rail level crossings? A systematic review and synthesis of findings using systems thinking","volume":"138","author":"Read","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_2","unstructured":"European Union Agency for Railways (2021, August 10). Safety Overview 2021. Available online: https:\/\/www.era.europa.eu\/library\/corporate-publications_en."},{"key":"ref_3","first-page":"149","article-title":"Evaluation of radar vehicle detection at four quadrant gate rail crossings","volume":"6","author":"Horne","year":"2016","journal-title":"J. Rail Transp. Plan. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"35718","DOI":"10.1109\/ACCESS.2021.3062220","article-title":"3D-LIDAR Based Object Detection and Tracking on the Edge of IoT for Railway Level Crossing","volume":"9","author":"Wisultschew","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.2298\/TSCI18S5551P","article-title":"Advanced thermal camera based system for object detection on rail tracks","volume":"22","author":"Ciric","year":"2018","journal-title":"Therm. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cai, H., Li, F., Gao, D., Yang, Y., Li, S., Gao, K., Qin, A., Hu, C., and Huang, Z. (2020, January 11\u201314). Foreign Objects Intrusion Detection Using Millimeter Wave Radar on Railway Crossings. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9282881"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep Learning for Computer Vision: A Brief Review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100204","DOI":"10.1016\/j.cosrev.2019.100204","article-title":"Background subtraction in real applications: Challenges, current models and future directions","volume":"35","author":"Bouwmans","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gajbhiye, P., Naveen, C., and Satpute, V.R. (2017, January 14\u201316). VIRTUe: Video surveillance for rail-road traffic safety at unmanned level crossings; (Incorporating Indian scenario). Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India.","DOI":"10.1109\/TENCONSpring.2017.8070015"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"548604","DOI":"10.1155\/2011\/548604","article-title":"3D Objects Localization Using Fuzzy Approach and Hierarchical Belief Propagation: Application at Level Crossings","volume":"2011","author":"Fakhfakh","year":"2011","journal-title":"EURASIP J. Image Video Process"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109853","DOI":"10.1016\/j.measurement.2021.109853","article-title":"A multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter","volume":"185","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.trc.2018.12.004","article-title":"Enhancing transportation systems via deep learning: A survey","volume":"99","author":"Wang","year":"2019","journal-title":"Transp. Res. Part CEmerg. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109241","DOI":"10.1016\/j.measurement.2021.109241","article-title":"Obstacle detection of rail transit based on deep learning","volume":"176","author":"He","year":"2021","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"170461","DOI":"10.1109\/ACCESS.2020.3021508","article-title":"Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review","volume":"8","author":"Aziz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016, January 11\u201314). A unified multi-scale deep convolutional neural network for fast object detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Translator","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_20","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_21","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sikora, P., Kiac, M., and Dutta, M.K. (2020, January 6\u20138). Classification of railway level crossing barrier and light signalling system using YOLOv3. Proceedings of the 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy.","DOI":"10.1109\/TSP49548.2020.9163535"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., and Li, D. (2020). An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification. Sensors, 20.","DOI":"10.3390\/s20071999"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15515","DOI":"10.1109\/JSEN.2020.3031861","article-title":"Artificial Intelligence-Based Surveillance System for Railway Crossing Traffic","volume":"21","author":"Sikora","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107090","DOI":"10.1016\/j.knosys.2021.107090","article-title":"Review on self-supervised image recognition using deep neural networks","volume":"224","author":"Ohri","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.robot.2017.11.014","article-title":"Unsupervised obstacle detection in driving environments using deep-learning-based stereovision","volume":"100","author":"Dairi","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TITS.2018.2836141","article-title":"Impact of Data Loss for Prediction of Traffic Flow on an Urban Road Using Neural Networks","volume":"20","author":"Pamula","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TITS.2018.2835308","article-title":"Deep Spatio-Temporal Representation for Detection of Road Accidents Using Stacked Autoencoder","volume":"20","author":"Singh","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1109\/TITS.2016.2545640","article-title":"Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features","volume":"17","author":"Siddiqui","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4052","DOI":"10.1109\/TITS.2019.2934574","article-title":"An Efficient Algorithm for Detection of Vacant Spaces in Delimited and Non-Delimited Parking Lots","volume":"21","author":"Varghese","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/TCI.2015.2424077","article-title":"FPGA-Based Parallel Hardware Architecture for Real-Time Image Classification","volume":"1","author":"Qasaimeh","year":"2015","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.sysarc.2019.01.011","article-title":"A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform","volume":"97","author":"Mittal","year":"2019","journal-title":"J. Syst. Arch."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:02:10Z","timestamp":1760166130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,18]]},"references-count":32,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21186281"],"URL":"https:\/\/doi.org\/10.3390\/s21186281","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,18]]}}}