{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:14:39Z","timestamp":1774862079275,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"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>In intelligent transportation systems and urban traffic management, accurate vehicle area intrusion detection based on surveillance imagery plays a critical role in ensuring road safety and operational efficiency. However, under real-world road surveillance conditions characterized by complex backgrounds, varying illumination, occlusion, and scale variations, mainstream detection algorithms often suffer from high false detection and missed detection rates, limiting their reliability and practical deployment. To address these challenges, this paper proposes YOLOv10-Intrusion, a high-precision vehicle area intrusion detection framework based on an improved version of YOLOv10s. The proposed algorithm incorporates Omni-Dimensional Dynamic Convolution (ODConv) and a custom-designed RCS_M module to enhance feature extraction and fine-grained recognition capability. In addition, a Bidirectional Feature Pyramid Network (BiFPN) is employed to optimize multi-scale feature fusion at the neck level. These improvements collectively reduce false detections and missed detections while improving model recall and mean Average Precision (mAP). Furthermore, the Wise-IoU (WIoU) loss function replaces the original Complete IoU (CIoU) loss to accelerate convergence and stabilize bounding box regression under complex surveillance conditions. A dedicated vehicle area intrusion dataset is constructed from real-world road surveillance footage, covering five vehicle categories across diverse road environments and lighting conditions. Experimental results demonstrate that, compared with the baseline YOLOv10s, YOLOv10-Intrusion achieves improvements of 1.5, 3.3, 3.6, and 2.8 percentage points in Precision, Recall, mAP@0.5, and mAP@0.5:0.95, respectively, and outperforms other mainstream detection algorithms in vehicle area intrusion detection tasks.<\/jats:p>","DOI":"10.3390\/s26072118","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:22:36Z","timestamp":1774858956000},"page":"2118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv10-Intrusion: An Improved YOLOv10-Based Algorithm for Vehicle Area Intrusion Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Chuanyue","family":"Jie","sequence":"first","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuyang","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113112","DOI":"10.1016\/j.engappai.2025.113112","article-title":"TIE-LR: A multi-module deep learning framework for traffic monitoring on low-resolution videos","volume":"163","author":"Liu","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aej.2024.09.012","article-title":"YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation","volume":"107","author":"Tang","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105944","DOI":"10.1016\/j.imavis.2026.105944","article-title":"All you need for object detection: From pixels, points, and prompts to Next-Gen fusion and multimodal LLMs\/VLMs in autonomous vehicles","volume":"169","author":"Boroujeni","year":"2026","journal-title":"Image Vis. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130132","DOI":"10.1016\/j.eswa.2025.130132","article-title":"Railway obstacle intrusion detection and risk assessment based on MSIA-YOLOv8 and DALNet","volume":"299","author":"Hu","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_5","first-page":"100571","article-title":"Railway obstacle intrusion warning mechanism integrating YOLO-based detection and risk assessment","volume":"38","author":"Zhang","year":"2024","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101707","DOI":"10.1016\/j.iot.2025.101707","article-title":"Real-time efficiency of YOLOv5 and YOLOv8 in human intrusion detection across diverse environments and recommendation","volume":"33","author":"Sodhro","year":"2025","journal-title":"Internet Things"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dong, S., Li, Y., Wang, W., Hu, Q., Zhang, R., and Wang, X. (J. Pipeline Sci. Eng., 2026). FPE-YOLO: Real-Time Small-Object Detection for Third-Party Intrusion in Oil and Gas Pipeline Integrity Monitoring, J. Pipeline Sci. Eng., In Press.","DOI":"10.1016\/j.jpse.2026.100466"},{"key":"ref_8","first-page":"6505","article-title":"Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification","volume":"74","author":"Alnfiai","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.engappai.2025.113338","article-title":"Real-time railroad crossing surveillance with edge computing","volume":"165","author":"Tang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6419","DOI":"10.1109\/TIV.2024.3367895","article-title":"MMID-Bench: A Comprehensive Benchmark for Multi-Domain Multi-Category Intrusion Detection","volume":"9","author":"Han","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112602","DOI":"10.1016\/j.measurement.2023.112602","article-title":"Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene","volume":"211","author":"Huang","year":"2023","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113893","DOI":"10.1016\/j.engappai.2026.113893","article-title":"Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles","volume":"168","author":"Omodaratan","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.procs.2021.02.031","article-title":"Real-time object detection based on YOLO-v2 for tiny vehicle object","volume":"183","author":"Han","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108942","DOI":"10.1016\/j.rineng.2025.108942","article-title":"Benchmarking YOLO-based deep learning models for real-time object detection in hybrid ADAS and intelligent transportation systems","volume":"29","author":"Chaman","year":"2026","journal-title":"Results Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.4018\/IJSWIS.330752","article-title":"MC-YOLO-Based Lightweight Detection Method for Nighttime Vehicle Images in a Semantic Web-Based Video Surveillance System","volume":"19","author":"Wang","year":"2023","journal-title":"Int. J. Semant. Web Inf. Syst."},{"key":"ref_16","first-page":"2667","article-title":"IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection","volume":"78","author":"Luo","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121209","DOI":"10.1016\/j.eswa.2023.121209","article-title":"YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection","volume":"237","author":"Kang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105565","DOI":"10.1016\/j.dsp.2025.105565","article-title":"DD-YOLO: A dual-channel dual-path YOLO network for target detection of blurred vehicles","volume":"168","author":"Duan","year":"2026","journal-title":"Digit. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105628","DOI":"10.1016\/j.dsp.2025.105628","article-title":"MLE-YOLO: A lightweight and robust vehicle and pedestrian detector for adverse weather in autonomous driving","volume":"168","author":"Du","year":"2026","journal-title":"Digit. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.icte.2025.09.002","article-title":"Hybrid-YOLO: Lightweight Mamba-Transformer Hybrid with multi-scale fusion for real-world traffic detection","volume":"12","author":"Wang","year":"2026","journal-title":"ICT Express"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"126837","DOI":"10.1016\/j.eswa.2025.126837","article-title":"VP-YOLO: A human visual perception-inspired robust vehicle-pedestrian detection model for complex traffic scenarios","volume":"274","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104632","DOI":"10.1016\/j.dsp.2024.104632","article-title":"YOLO-CCS: Vehicle detection algorithm based on coordinate attention mechanism","volume":"153","author":"Li","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105276","DOI":"10.1016\/j.imavis.2024.105276","article-title":"LVD-YOLO: An efficient lightweight vehicle detection model for intelligent transportation systems","volume":"151","author":"Pan","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1016\/j.procs.2024.09.392","article-title":"Real-time Object Detection in Autonomous Vehicles with YOLO","volume":"246","author":"Alahdal","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.dt.2019.10.005","article-title":"An infrared target intrusion detection method based on feature fusion and enhancement","volume":"16","author":"Hu","year":"2020","journal-title":"Def. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127063","DOI":"10.1016\/j.jnc.2025.127063","article-title":"Deep-track: A real-time animal detection and monitoring system for mitigating human-wildlife conflict in fringe areas","volume":"88","author":"Balakrishnan","year":"2025","journal-title":"J. Nat. Conserv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1108\/IJICC-06-2025-0370","article-title":"IMV-YOLO: Infrared multi-angle vehicle real-time detection network based YOLOv11 for adverse weather conditions","volume":"18","author":"Lu","year":"2025","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_28","first-page":"32","article-title":"Visual Detection Algorithms for Counter-UAV in Low-Altitude Air Defense","volume":"86","author":"Li","year":"2026","journal-title":"Comput. Mater. Contin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106068","DOI":"10.1016\/j.autcon.2025.106068","article-title":"Real-time defect detection in underground sewage pipelines using an improved YOLOv5 model","volume":"173","author":"Lu","year":"2025","journal-title":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121366","DOI":"10.1016\/j.ins.2024.121366","article-title":"LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle","volume":"686","author":"Fan","year":"2025","journal-title":"Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105905","DOI":"10.1016\/j.infrared.2025.105905","article-title":"NOC-YOLO: An exploration to enhance small-target vehicle detection accuracy in aerial infrared images","volume":"149","author":"Zhang","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"ref_32","first-page":"5761","article-title":"MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes","volume":"75","author":"Song","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104857","DOI":"10.1016\/j.dsp.2024.104857","article-title":"PV-YOLO: A lightweight pedestrian and vehicle detection model based on improved YOLOv8","volume":"156","author":"Liu","year":"2025","journal-title":"Digit. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"127089","DOI":"10.1016\/j.eswa.2025.127089","article-title":"LiDAR-Camera joint obstacle detection algorithm for railway track area","volume":"275","author":"Nan","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105070","DOI":"10.1016\/j.imavis.2024.105070","article-title":"Detection of objects in satellite and aerial imagery using channel and spatially attentive YOLO-CSL for surveillance","volume":"147","author":"Chaurasia","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104560","DOI":"10.1016\/j.jvcir.2025.104560","article-title":"MP-YOLO: Multidimensional feature fusion based layer adaptive pruning YOLO for dense vehicle object detection algorithm","volume":"112","author":"Zhou","year":"2025","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104701","DOI":"10.1016\/j.jvcir.2025.104701","article-title":"PV3M-YOLO: A triple attention-enhanced model for detecting pedestrians and vehicles in UAV-enabled smart transport networks","volume":"115","author":"Tahir","year":"2026","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_38","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_40","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, January 10). Ultralytics YOLOv8. Version 8.0.0, AGPL-3.0 License. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_41","unstructured":"Jocher, G., and Qiu, J. (2024, September 30). Ultralytics YOLO11. Version 11.0.0, AGPL-3.0 License. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_42","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv."},{"key":"ref_43","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021). Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kong, Y., Shang, X., and Jia, S. (2024). Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model. Sensors, 24.","DOI":"10.3390\/s24175496"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"14389","DOI":"10.1038\/s41598-024-65272-1","article-title":"Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition","volume":"14","author":"Moussaoui","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103289","DOI":"10.1016\/j.displa.2025.103289","article-title":"Teacher-student adversarial YOLO for domain adaptive detection in traffic scenes under adverse weather","volume":"92","author":"Han","year":"2025","journal-title":"Displays"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:29:55Z","timestamp":1774859395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,29]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26072118"],"URL":"https:\/\/doi.org\/10.3390\/s26072118","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,29]]}}}