{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T15:20:21Z","timestamp":1780327221614,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Project of China Railway","award":["L2024G004"],"award-info":[{"award-number":["L2024G004"]}]},{"name":"State Key Laboratory of Advanced Rail Autonomous Operation","award":["RAO2025K07"],"award-info":[{"award-number":["RAO2025K07"]}]},{"name":"Youth Research Project of the Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited","award":["2025TH01"],"award-info":[{"award-number":["2025TH01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional signal equipment is incapable of real-time monitoring of foreign objects intruding into track zones. To effectively ensure the operational safety of trains, this paper presents an intelligent obstacle detection approach of visual sensing for railway track regions based on YOLOv8, named ACX-YOLOv8. Built upon the baseline YOLOv8 framework, the proposed method first incorporates the spatial coordinate attention mechanism (SCAM) to enhance the model\u2019s ability to capture long-range dependencies and local fine-grained details, thereby improving its perceptual capacity and feature representation performance. Subsequently, the cascaded dilated convolution (CDConv) module is integrated to effectively extract multi-scale image features, strengthening the model\u2019s capability to identify foreign objects in complex railway environments. Finally, an X6 decoupled detection head is devised to further elevate the model\u2019s detection accuracy and inference efficiency. Field experiments in real-world scenarios are conducted to validate the effectiveness of the improved algorithm. Experimental results demonstrate that the optimized ACX-YOLOv8 model has a total parameter count of 4.85 million and achieves a mean average precision at IoU = 0.5 (mAP50) of 87.1% on the test dataset, which is a 2.7% improvement over the original YOLOv8 baseline model. The lightweight property and detection precision of the model are both effectively guaranteed. Furthermore, to verify the generalization ability of the algorithm, tests are performed on the public PASCAL VOC dataset, where the mAP50 value is increased by 1.8%. These findings indicate that the ACX-YOLOv8 algorithm can detect various foreign objects invading railway track areas rapidly and accurately. It provides efficient and reliable technical support for real-time obstacle monitoring in complex and variable railway track environments and contributes to enhancing the safety and intelligentization level of railway operations.<\/jats:p>","DOI":"10.3390\/s26051673","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:05:44Z","timestamp":1772795144000},"page":"1673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Intelligent Obstacle Detection Method for Rail Transit Scenarios"],"prefix":"10.3390","volume":"26","author":[{"given":"Zhao","family":"Sheng","sequence":"first","affiliation":[{"name":"Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Cooperation and Development Department, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Shangguan","sequence":"additional","affiliation":[{"name":"School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijing","family":"Wang","sequence":"additional","affiliation":[{"name":"Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yige","family":"Wang","sequence":"additional","affiliation":[{"name":"Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyu","family":"He","sequence":"additional","affiliation":[{"name":"Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"ref_1","unstructured":"(2022). \u201c14th Five-Year Plan\u201d Railway Standardization Development Plan. Railw. Technol. Superv., 50, 4\u201320."},{"key":"ref_2","first-page":"528","article-title":"Research on Railway Obstacle Detection Based on YOLO v4","volume":"19","author":"Liu","year":"2022","journal-title":"J. Railw. Sci. Eng."},{"key":"ref_3","first-page":"58","article-title":"Research on Embedded Railway Foreign Body Intrusion Detection Algorithm Based on Target Recognition and Tracking","volume":"37","author":"Shi","year":"2015","journal-title":"J. Railw. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.tranpol.2006.11.004","article-title":"Reevaluation of Japanese high-speed rail construction: Recent situation of the north corridor Shinkansen and its way to completion","volume":"14","author":"Nakagawa","year":"2007","journal-title":"Transp. Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18268","DOI":"10.3390\/s141018268","article-title":"An intrusion detection system for the protection of railway assets using fiber Bragg grating sensors","volume":"14","author":"Catalano","year":"2014","journal-title":"Sensors"},{"key":"ref_6","first-page":"24","article-title":"Application of fiber Bragg grating in the monitoring system for foreign object intrusion in high-speed railways","volume":"6","author":"Sun","year":"2012","journal-title":"Inf. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/JSEN.2015.2490247","article-title":"Obstacle detection on railway tracks using vibration sensors and signal filtering using Bayesian analysis","volume":"16","author":"Sinha","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_8","unstructured":"Liu, K., Li, L., and Tan, F. (2019). A Design of Intelligent Foreign Object Intrusion Detection System in Subway Station Track Area. Proceedings of the Sixth International Conference on Transportation Engineering, American Society of Civil Engineers."},{"key":"ref_9","first-page":"1371","article-title":"High-speed Railway Foreign Object Intrusion Detection Algorithm Based on Fast Background Difference","volume":"37","author":"Guo","year":"2016","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_10","unstructured":"Zhang, L.H. (2021). Research on Railway Track Foreign Object Intrusion Detection and Tracking Algorithm. [Master\u2019s Thesis, Zhengzhou University]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_12","unstructured":"Goodfellow, I., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_13","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_20","unstructured":"Tao, H.Q. (2020). Research on High-speed Railway Foreign Object Intrusion Detection Algorithm Based on Faster R-CNN. [Master\u2019s Thesis, Lanzhou Jiaotong University]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5012611","DOI":"10.1109\/TIM.2022.3176901","article-title":"A Lightweight NMSFree Framework for Real-Time Visual Fault Detection System of Freight Trains","volume":"71","author":"Sun","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111277","DOI":"10.1016\/j.measurement.2022.111277","article-title":"Urban Rail Transit Obstacle Detection Based on Improved R-CNN","volume":"196","author":"He","year":"2022","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_24","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer International Publishing. Proceedings, Part I 14."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, X., Hua, X., Xiao, F., Li, Y., Hu, X., and Sun, P. (2018). Multi-object detection in traffic scenes based on improved SSD. Electronics, 7.","DOI":"10.3390\/electronics7110302"},{"key":"ref_26","first-page":"91","article-title":"Research on Railway Foreign Object Intrusion Detection Algorithm Based on Faster R-CNN Network Model","volume":"42","author":"Xu","year":"2020","journal-title":"J. China Railw. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5009116","DOI":"10.1109\/TIM.2022.3150584","article-title":"A lightweight framework for obstacle detection in the railway image based on fast region proposal and improved YOLO-tiny network","volume":"71","author":"Guan","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Meng, C., Wang, Z., Shi, L., Gao, Y., Tao, Y., and Wei, L. (2023). SDRC-YOLO: A novel foreign object intrusion detection algorithm in railway scenarios. Electronics, 12.","DOI":"10.3390\/electronics12051256"},{"key":"ref_29","first-page":"729","article-title":"Real-time detection of railway cracks based on improved YOLOXNano","volume":"Volume 12566","author":"Du","year":"2023","journal-title":"Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)"},{"key":"ref_30","first-page":"1099","article-title":"High-speed Railway Foreign Object Intrusion Detection Algorithm Based on Improved YOLOv7","volume":"54","author":"Yan","year":"2024","journal-title":"Radio Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4795396","DOI":"10.1155\/2021\/4795396","article-title":"Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD","volume":"2021","author":"Zhang","year":"2021","journal-title":"Complexity"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, X., and Gao, J. (2021). Surface defect detection method of hot rolling strip based on improved SSD model. Database Systems for Advanced Applications. DASFAA 2021 International Workshops: BDQM, GDMA, MLDLDSA, MobiSocial, and MUST, Taipei, Taiwan, 11\u201314 April 2021, Proceedings 26, Springer International Publishing.","DOI":"10.1007\/978-3-030-73216-5_15"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11554-023-01406-4","article-title":"MCX-YOLOv5: Efficient helmet detection in complex power warehouse scenarios","volume":"21","author":"Xu","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yanan, S., Hui, Z., Li, L., and Hang, Z. (2018). Rail surface defect detection method based on YOLOv3 deep learning networks. Proceedings of the 2018 Chinese Automation Congress (CAC), IEEE.","DOI":"10.1109\/CAC.2018.8623082"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, Q., Tang, B., Jiang, L., Zhu, F., and Zhao, X. (2022). Rail surface defects detection based on YOLO v5 integrated with transformer. Proceedings of the 2022 IEEE 5th International Conference on Electronics Technology (ICET), IEEE.","DOI":"10.1109\/ICET55676.2022.9824255"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, F., Dong, D., Jia, X., Guo, J., and Yu, X. (2024). Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts. Agronomy, 14.","DOI":"10.3390\/agronomy14102412"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"175926","DOI":"10.1109\/ACCESS.2024.3504010","article-title":"Research on foreign object intrusion detection for railway tracks utilizing risk assessment and YOLO detection","volume":"12","author":"Ning","year":"2024","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1673\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:12:21Z","timestamp":1772795541000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26051673"],"URL":"https:\/\/doi.org\/10.3390\/s26051673","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,6]]}}}