A Vehicle Detector Based on Feature Focused Diffusion

Abstract

Vehicle detection technology is one of the basic and key technologies for realizing intelligent transportation and autonomous driving. However, in real scenes there are effects such as lighting shadows, motion blur, and target occlusion. This paper proposes a You Only Look Once Version 8 (YOLOv8) vehicle detector Re-Diffusion Task-You Only Look Once (RDT-YOLO) based on feature focused diffusion, aiming to meet the challenge of vehicle detection in complex scenes. A new RepGhost cross stage partial effective long-range aggregation network (RGC-ELAN), focusing diffusion dimension-aware (FDDA) pyramid network, and task align dynamic (TAD) detection head were designed based on the original structure. Experimental results show that RDT-YOLO’s F1 Score increased by 6.0% and the mean Average Precision (mAP) increased by 3.9%. Moreover, the calculation parameters of RDT-YOLO were reduced by 23.6%, the model size was reduced by 20.9%, and the running speed reached 66.2 frames per second (FPS). Additional generalization experiments and robustness tests showed that RDT-YOLO has broad application prospects in different scenarios and can provide reliable support for intelligent transportation systems and autonomous driving technologies.