计算机工程与应用2024，Vol.60Issue(20)：207-214,8.DOI:10.3778/j.issn.1002-8331.2406-0204
Haar小波下采样优化YOLOv9的道路车辆和行人检测
Road Vehicle and Pedestrian Detection Based on YOLOv9 for Haar Wavelet Downsampling
摘要
Abstract
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.关键词
小目标检测/车辆行人/YOLOv9/深度学习/Haar小波下采样Key words
small object detection/vehicles and pedestrians/YOLOv9/deep learning/Haar wavelet downsampling(HWD)分类
信息技术与安全科学引用本文复制引用
李琳,靳志鑫,俞晓磊,王安红..Haar小波下采样优化YOLOv9的道路车辆和行人检测[J].计算机工程与应用,2024,60(20):207-214,8.基金项目
国家自然科学基金联合基金重点项目(U23A20314) （U23A20314）
国家自然科学基金(62072325,61771240) （62072325,61771240）
中国博士后科学基金(2022M711620) （2022M711620）
太原科技大学科研启动基金(2024072). （2024072）
