Haar小波下采样优化YOLOv9的道路车辆和行人检测OA北大核心CSTPCD

In the current background of intelligence and informatization,the YOLOv9 algorithm based on Haar wavelet downsampling(HWD)is proposed for vehicle and pedestrian target detection in complex environments with autonomous driving mode to intelligently collect pedestrian and vehicle targets on the road.The operation of Haar wavelet downsam-pling reduces the spatial resolution of feature maps and preserves detailed information such as edges and textures as much as possible,effectively reducing the uncertainty of information.By utilizing the sum of cross entropy loss and generalized dice loss as the loss function of the network,the difference between probability distributions can be effectively measured,and dice loss calculations can be performed pixel by pixel,making it easier to optimize the network.The experimental results show that the average accuracy of the proposed model reaches 95.86%,and the detection frame rate reaches 179 FPS on the KITTY dataset.Compared with YOLOv9,the improved algorithm can accurately identify vehicles and pedestrians of different scales on complex roads,which not only improves the redundancy of computational capacity and missed detection of small targets in the original detection algorithm,but also provides visual technology support for intelligent autonomous driving.