车辆检测是智能交通系统和自动驾驶的重要组成部分。然而，实际交通场景中存在许多不确定因素，导致车辆检测模型的准确率低实时性差。为了解决这个问题，提出了一种快速准确的车辆检测算法——YOLOv8-DEL。使用DGCST(dynamic group convolution shuffle transformer)模块代替C2f模块来重构主干网络，以增强特征提取能力并使网络更轻量;添加的P2检测层能使模型更敏锐地定位和检测小目标，同时采用Efficient RepGFPN进行多尺度特征融合，以丰富特征信息并提高模型的特征表达能力;通过结合GroupNorm和共享卷积的优点，设计了一种轻量型共享卷积检测头，在保持精度的前提下，有效减少参数量并提升检测速度。与YOLOv8相比，提出的YOLOv8-DEL在BDD100K数据集和KITTI数据集上，mAP@0。5分别提高了 4。8个百分点和1。2个百分点，具有实时检测速度(208。6 FPS和216。4 FPS)，在检测精度和速度方面实现了更有利的折中。
YOLOv8-DEL:Research on Real-Time Vehicle Detection Algorithm Based on Improved YOLOv8n
Vehicle detection is of great significance for intelligent transportation system and automatic driving technology.However,actual traffic scene suffers from many uncertain factors,leading to the low accuracy of vehicle detection and poor real-time performance.To solve this problem,this paper proposes a fast and accurate vehicle detection algorithm,named YOLOv8-DEL.First,dynamic group convolution shuffle transformer(DGCST)is used to replace several C2f module to enhance the ability of network feature extraction and make the backbone network lighter.Then a P2 detection layer is introduced to make the model more sensitive to locate and detect small targets,while Efficient RepGFPN is adopted for multi-dimensional feature fusion to strengthen the feature expression ability.Finally,a lightweight shared convolution detection head is proposed,which can reduce the model parameters and improve the detection speed while lossless accuracy.The experimental results demonstrate that compared with the YOLOv8,YOLOv8-DEL improves mAP@0.5 by 4.8 percentage points and 1.2 percentage points on BDD100K dataset and KITTI dataset with real-time detection speed(208.6 FPS and 216.4 FPS).YOLOv8-DEL achieves a better trade-off between detection accuracy and speed.
vehicle detectionYOLOv8dynamic group convolution shuffle transformer(DGCST)Efficient RepGFPNlightweight detection head
万方数据
维普
