Vehicle detection and identification method based on improved DSOD model

The invention discloses a vehicle detection and identification method based on an improved DSOD model. The DSOD is improved on the basis of an SSD algorithm, can be simply understood as SSD + DenseNet= DSOD, adopts proposal-free detection model SSD, and adds a DenseNet thought. The DSOD model is divided into two parts, namely a Backbone for feature extraction and a Front-end for target prediction. The Backbone sub-network is similar to a DenseNet, is composed of a layer of Stem block (main module), four layers of Dense blocks (Dense modules), two layers of Translation layers (transition layers) and two layers of Translation w/o pooling layers (transition w/o pooling layers), and is used for extracting image features. The Fort_end sub-network_network (front-end detection sub-network) implements a border frame detection effect through Dense Connetion. 本发明公开了一种基于改进的DSOD模型的车辆检测与识别方法，DSOD是在SSD算法的基础上进行改进的，可以简单理解为SSD+DenseNet＝DSOD，采用proposal-free的检测模型SSD，加入DenseNet的思想。DSOD模型分成两个部分：用于特征提取的Backbone，用于目标预测的Front-end。Ba