Research On Binocular Vision Vehicle Detection Method Based On Improved Stereo R-CNN

In order to realize traffic intelligence,vehicle detection as the main technology has received great attention,and has been widely used in the field of road monitoring and automatic driving.However,there are still many challenges in vehicle detection,especially the problems of occlusion,truncation,long-distance small target and so on.Therefore,this paper designs a vehicle detection system based on the Stereo R-CNN network to effectively detect vehicles in the road scene.This paper mainly studies the binocular vision vehicle detection method,and chooses the Stereo R-CNN with good accuracy in vehicle detection as the basic model.Stereo R-CNN is a dual-branch network for stereo input,which can associate and detect objects in the image pair at the same time.However,there are two problems in this algorithm: 1)The small convolution kernel is used in the convolution neural network of Stereo R-CNN for feature extraction,so it is difficult to correlate long-distance pixels.2)The depth information in 3D scene is lost in image data set.This paper focuses on the above two problems.1.For the backbone network of Stereo R-CNN detection algorithm,it is difficult to calculate the long-range correlation and obtain the global information.Combined with attention mechanism,this paper proposes a Stereo R-CNN target detection algorithm based on attention residual network.Taking the residual network as the benchmark model,the CBAM attention module is introduced on this basis.The purpose is to use the attention module to adjust the weight and response strength in the channel dimension and spatial dimension in the process of network feature extraction,and obtain the correlation between the long range pixels in the feature graph,so as to enhance the representation ability of the network.2.The data set used by the Stereo R-CNN algorithm is a two-dimensional image,and the three-dimensional information of the object is lost.In this paper,combining thesemi-global stereo matching algorithm,a Stereo R-CNN target detection algorithm basedon semi-global matching is proposed.The algorithm recovers the depth information bycalculating the position difference of the corresponding pixels,Then use the depth information and the left image as the input of the Stereo R-CNN network for training.Stereo R-CNN can use three-dimensional information for detection,which enriches the available information.To sum up,A large number of experiments are carried out on ktiit binocular data set,and the experimental results certificate the availabillty of the proposed method,which can improve the accuracy of vehicle detection performance in 2D and 3D.From the visual comparison of the detection results,it can be seen that the detection effect of this method is enhanced for the small target vehicle and the truncated vehicle.This paper provides a new theoretical solution for vehicle detection,and also provides a favorable guarantee for the realization of intelligent transportation.