Application of Convolution Neural Network inVehicle Identification System

In the view of the special requirements of separate detection and identification for vehicles of the unmanned access control system of factory buildings and warehouses, a vehicle detection and identification model based on Darknet19 network and SSD (single shot-multibox detector) model was proposed. Firstly, by collecting a large number of images in the real scene including pedestrians, forklifts, trucks and manual labeling, a private data set was constructed. Secondly, the dataset of ImageNet2012 was employed to retrain the Darknet19 network under the framework of Caffe. Besides, the SSD target detection model was improved by replacing the basic classification network and adding a batch normalization layer after each convolution layer to construct a new endto-end vehicle detection model. The results show that the detection accuracy of the improved algorithm for truck can reach 99.2%, the detection speed can reach 72 frames/s, and the accuracy and real-time performance can meet the requirements of vehicle detection in storage environment.