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dc.contributor.authorXie, Yuchen
dc.contributor.authorDu, Danfeng
dc.contributor.authorLiu, Yang
dc.date.accessioned2026-08-20T21:22:39Z
dc.date.available2026-08-20T21:22:39Z
dc.date.copyright2025/07/24
dc.date.issued2025
dc.identifier.otherJCCEE5.CPENG-6164.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314360
dc.description.abstractAbstractVehicle detection tasks in autonomous driving scenarios face challenges such as illumination changes, motion blur, and target occlusion. This paper proposes a vehicle detector RepViT efficient-you only look once (RE-YOLO) for autonomous driving ...Practical ApplicationsThe ability of autonomous vehicles to detect other vehicles quickly and accurately is crucial for ensuring road safety and improving traffic efficiency. This research has significant real-world applications in the field of self-...
dc.publisherAmerican Society of Civil Engineers
dc.titleRE-YOLO: Vehicle Detector Based on Knowledge Distillation YOLOv8 for Autonomous Driving Scenarios
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue6
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6164
dc.journal.fristpage04025084-1
dc.journal.lastpage04025084-16
dc.page16
dc.treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 006
dc.contenttypeFulltext


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