{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:36:12Z","timestamp":1784644572591,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"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 modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.<\/jats:p>","DOI":"10.3390\/s24186116","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7129-9155","authenticated-orcid":false,"given":"Shi","family":"Lei","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Batangas State University, Batangas City 4200, Philippines"},{"name":"College of Electrical and Control Engineering, Henan University of Urban Construction, Pingdingshan City 467000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Yi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Batangas State University, Batangas City 4200, Philippines"},{"name":"College of Electrical and Control Engineering, Henan University of Urban Construction, Pingdingshan City 467000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7551-7181","authenticated-orcid":false,"given":"Jeffrey S.","family":"Sarmiento","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Batangas State University, Batangas City 4200, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1007\/s11554-024-01507-8","article-title":"An improved multi-scale and knowledge distillation method for efficient pedestrian detection in dense scenes","volume":"21","author":"Xu","year":"2024","journal-title":"J. Real Time Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fang, Y., and Pang, H. (2024). An Improved Pedestrian Detection Model Based on YOLOv8 for Dense Scenes. Symmetry, 16.","DOI":"10.3390\/sym16060716"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, Y.Y., Xu, W., Wang, H., and Hu, L. (2024). Vehicle\u2013Pedestrian Detection Method Based on Improved YOLOv8. Electronics, 13.","DOI":"10.3390\/electronics13112149"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5782","DOI":"10.3934\/mbe.2024255","article-title":"Research on a vehicle and pedestrian detection algorithm based on improved attention and feature fusion","volume":"21","author":"Liang","year":"2024","journal-title":"Math. Biosci. Eng. MBE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1002\/tee.24075","article-title":"Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7-Tiny","volume":"19","author":"Wang","year":"2024","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_6","first-page":"90","article-title":"Pedestrian and vehicle detection method in infrared scene based on improved YOLOv5s model","volume":"5","author":"Yang","year":"2024","journal-title":"Autom. Mach. Learn."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tahir, N.U.A., Long, Z., Zhang, Z., Asim, M., and ELAffendi, M. (2024). PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8. Drones, 8.","DOI":"10.3390\/drones8030084"},{"key":"ref_8","first-page":"1","article-title":"A Method of Lightweight Pedestrian Detection in Rainy and Snowy Weather Based on Improved YOLOv5","volume":"7","author":"Gao","year":"2024","journal-title":"Acad. J. Comput. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1007\/s11760-023-02896-2","article-title":"IDPD: Improved deformable-DETR for crowd pedestrian detection","volume":"18","author":"Han","year":"2023","journal-title":"Signal Image Video Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"178","DOI":"10.21595\/mme.2023.23719","article-title":"Research on lightweight pedestrian detection based on improved YOLOv5","volume":"9","author":"Jin","year":"2023","journal-title":"Math. Models Eng."},{"key":"ref_11","first-page":"1401","article-title":"Vehicle And Pedestrian Detection Algorithm Based on Improved YOLOv5","volume":"50","author":"Sun","year":"2023","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, Y., Zhang, Y., Zong, M., and Zhu, J. (2023). Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection. Sensors, 23.","DOI":"10.3390\/s23229089"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s11801-023-3078-x","article-title":"Vehicle and pedestrian detection method based on improved YOLOv4-tiny","volume":"19","author":"Li","year":"2023","journal-title":"Optoelectron. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113442","DOI":"10.1016\/j.measurement.2023.113442","article-title":"Infrared pedestrian detection using improved UNet and YOLO through sharing visible light domain information","volume":"221","author":"Wei","year":"2023","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., and Liu, X. (2023). An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians. Sensors, 23.","DOI":"10.3390\/s23135912"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s11554-023-01287-7","article-title":"Improved YOLOX for pedestrian detection in crowded scenes","volume":"20","author":"Gao","year":"2023","journal-title":"J. Real Time Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, M.L., Sun, G.B., and Yu, J.X. (2023). A Pedestrian Detection Network Model Based on Improved YOLOv5. Entropy, 25.","DOI":"10.3390\/e25020381"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1049\/cvi2.12159","article-title":"Improving multispectral pedestrian detection with scale-aware permutation attention and adjacent feature aggregation","volume":"17","author":"Zuo","year":"2022","journal-title":"IET Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1500428","DOI":"10.1155\/2022\/1500428","article-title":"Improved SSD Model for Pedestrian Detection in Natural Scene","volume":"2022","author":"Hong","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1177\/00202940221110164","article-title":"Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function","volume":"55","author":"Zhang","year":"2022","journal-title":"Meas. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6106853","DOI":"10.1155\/2022\/6106853","article-title":"Improved YOLOv4 for Pedestrian Detection and Counting in UAV Images","volume":"2022","author":"Kong","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2250034","DOI":"10.1142\/S0218001422500343","article-title":"Improving Single-Stage Object Detectors for Nighttime Pedestrian Detection","volume":"36","author":"Devi","year":"2022","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"219","DOI":"10.5194\/isprs-annals-V-4-2022-219-2022","article-title":"Improving 3d pedestrian detection for wearable sensor data with 2d human pose","volume":"V-4-2022","author":"Kamalasanan","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s13735-022-00239-4","article-title":"InceptionDepth-wiseYOLOv2: Improved implementation of YOLO framework for pedestrian detection","volume":"11","author":"Panigrahi","year":"2022","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shao, Y., Zhang, X., Chu, H., Zhang, X., Zhang, D., and Rao, Y. (2022). AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning. Appl. Sci., 12.","DOI":"10.3390\/app12073627"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tan, F., Xia, Z., Ma, Y., and Feng, X. (2022). 3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14030645"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"03020","DOI":"10.1051\/matecconf\/202235503020","article-title":"A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm","volume":"355","author":"Mao","year":"2022","journal-title":"MATEC Web Conf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9325803","DOI":"10.1155\/2022\/9325803","article-title":"Small-Scale and Occluded Pedestrian Detection Using Multi Mapping Feature Extraction Function and Modified Soft-NMS","volume":"2022","author":"Assefa","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11554-021-01183-y","article-title":"Real-time high-precision pedestrian tracking: A detection\u2013tracking\u2013correction strategy based on improved SSD and Cascade R-CNN","volume":"19","author":"Yang","year":"2021","journal-title":"J. Real Time Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012008","DOI":"10.1088\/1742-6596\/2078\/1\/012008","article-title":"Pedestrian detection algorithm based on improved muti-scale feature fusion","volume":"2078","author":"Hui","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3504615","DOI":"10.1109\/TIM.2023.3335509","article-title":"KDBiDet: A Bi-Branch Collaborative Training Algorithm Based on Knowledge Distillation for Photovoltaic Hot-Spot Detection Systems","volume":"73","author":"Hao","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1109\/TPWRD.2023.3274823","article-title":"PKAMNet: A Transmission Line Insulator Parallel-Gap Fault Detection Network Based on Prior Knowledge Transfer and Attention Mechanism","volume":"38","author":"Hao","year":"2023","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104660","DOI":"10.1016\/j.infrared.2023.104660","article-title":"Anchor-free infrared pedestrian detection based on cross-scale feature fusion and hierarchical attention mechanism","volume":"131","author":"Hao","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_34","unstructured":"(2024, September 18). Cityscapes 3D Benchmark Online. Available online: https:\/\/www.cityscapes-dataset.com\/Cityscapes."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:02:00Z","timestamp":1760112120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"references-count":34,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186116"],"URL":"https:\/\/doi.org\/10.3390\/s24186116","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,22]]}}}