Pedestrian and vehicle image recognition method based on deep learning

In recent years, the application of automobile driver assistance system has improved road traffic safety. However, the capability of traditional target recognition model is limited in the complex and changeable situation of actual road, which often leads to the confusion of pedestrian and vehicle target recognition results. In order to realize the joint detection of pedestrians and vehicles, a deep neural network model suitable for pedestrian and vehicle detection is established based on the object detection method of fast regional convolutional neural network. Aiming at the problems of frequent false detection and missing detection, poor detection effect of small-size targets, complex and changeable background environment, etc., a variety of network improvement schemes such as edge extraction of pedestrian and vehicle images, multi-layer feature fusion and multi-target candidate region input are designed respectively, so as to improve the detection effect of pedestrian and vehicle targets.