The traditional vehicle target detection problem is typically divided into two steps:the first step is generating assumptions

that is

the image may exist in the vehicle target

thus reducing the need to calculate the area; the second step is verifying the hypothesis

that is

testing to verify whether there is a vehicle target in the image. In the first step

different features must be designed for different scenes. Among the features commonly used in vehicle detection problems are symmetry

color

shadows

corners

edges

textures

and lights. In the second step

verifying the hypothesis is typically based on the template method or on the appearance of the characteristics of the method. In addition to the above basic features

HOG

Harris

SIFT

and other manual features are also typically used. Finally

the test results are obtained through the support vector machine and other classifiers that classify the feature matrix. The whole process appears to be very detrimental to the generalization of detection problems; thus

it is necessary to select suitable characteristics for the case of unreasonable samples. This paper proposes a vehicle detection method based on Fast R-CNN

which can find vehicle objects in scene images. The method is based on the idea of deep learning convolution neural network. First

define the visual task using the vehicle image to be detected. The candidate region of the sample image is obtained by the selective search algorithm

and the candidate region coordinates are inputted to the network learning together with the visual task sample image. The sample image is calculated by the convolution layer and the pool layer in the deep convolution neural network. Finally

the deep convolution feature is obtained. The specifications of the sample image are not specified at the time of input

and the convolution characteristics obtained at this time are variable. Subsequently

the feature is normalized by the pooling region of the region of interest based on the Fast R-CNN network structure. Finally

the feature is inputted into different full-connection branches

parallel regression calculation feature classification

and detection frame coordinate values. After several iterations and training

the target detection model

which is strongly related to the specified visual task is obtained

and the trained weight parameters are trained. In a new scene image

a given type of vehicle target can be detected by the target detection model. The method uses a test dataset that is relative to the vision task to test the object detection model. The experiment suggests that it will achieve effective detection result by the vehicle detection model in the situation where the scenes of test samples are strongly correlated to the vision task. Conclusion First

determine the visual tasks that include the bus and car as the two categories. The background scene is the city road. Experimental results show that when the correlation between the test sample scene and the visual task is higher and the deformation of the vehicle target in the sample is smaller

the vehicle target detection model is obtained for the vehicle. Target detection has a good detection effect. The vehicle target detection method proposed in this paper uses the convolution neural network to extract the convolution feature instead of the traditional manual feature extraction process. Fast R-CNN obtained the vehicle target detection model with good effect on the visual task training defined by the sample image. The model can achieve well-performing vehicle target detection on new scene images that are strongly related to visual tasks. In this paper

the convolution characteristics are used to replace the traditional manual features in combination with the depth of learning convolution neural network to avoid the traditional detection problems in the feature selection problem. Deep convolution features have better expression ability. Based on the Fast R-CNN network

the vehicle detection model is obtained by several iterations. The detection model has a good detection effect on the visual task specified in this paper. This paper provides a more general and concise solution of vehicle detection problems.