Autonomous driving paper index
A Vehicle Detector Based on Feature Focused Diffusion
One-line summary
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.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
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.
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