A lightweight vehicles detection network model based on YOLOv5

Abstract

For the existing target detection algorithm with large number of parameters and computation, which cannot be broadly applicable in the vehicle detection field, a lightweight vehicle detection network model is designed in this paper. The vehicle detection model mainly uses RFB-PANet to replace the PANet feature fusion network of YOLOv5s to obtain rich feature information to make the model more accurate, and uses GhostConv structure to optimize the traditional convolutional layer of YOLOv5s to have less parameters and computation of the model, so as to achieve the purpose of lightweights vehicle detection model. The experimental results show that proposed vehicle detection network model reduces the number of parameters by 25.0% and the computational effort by 18.75% compared with the original YOLOV5 network model. The mAp is increased by 2.1% and 1.2% on Pascal VOC dataset and vehicle dataset, respectively, thus achieving a good balance of detection accuracy and light weight