计算机应用与软件2023，Vol.40Issue(12)：169-175,7.DOI:10.3969/j.issn.1000-386x.2023.12.025
基于改进的YOLOv3多目标小尺度车辆检测算法研究
MULTI-TARGET SMALL-SCALE VEHICLE DETECTION ALGORITHM BASED ON IMPROVED YOLOV3
摘要
Abstract
Aimed at the problems of low efficiency of traditional vehicle detection algorithms,high missed detection rate,and poor detection of small target vehicles,an improved YOLOv3 vehicle detection algorithm is proposed.K-means++was used to cluster the training tags to determine the Anchor box for vehicle detection.EfficientNet was used with stronger feature extraction capabilities as the feature network,and 4 feature scales were used to fuse deep semantic information and shallow position information thus improving the detection efficiency of small-scale vehicles.CIoU and Focal loss functions were introduced to improve the network convergence speed and detection accuracy.Experimental results show that on the UA-DETRAC and self-built data sets,the MAP,Recall and FPS of the proposed algorithm reach 90.9%,88.3%and 30 frames per second respectively,which improves the detection accuracy of small target vehicle.关键词
车辆检测/YOLOv3/深度学习/EfficientNetKey words
Vehicle detetection/YOLOv3/Deep learning/EfficientNet分类
信息技术与安全科学引用本文复制引用
田智慧,杨奇文,魏海涛..基于改进的YOLOv3多目标小尺度车辆检测算法研究[J].计算机应用与软件,2023,40(12):169-175,7.基金项目
国家重点研发计划项目(2018YFB0505004-03). （2018YFB0505004-03）
