Forward Vehicle Detection And Ranging Research Based On Convolutional Neural Network

With the continuous development of technology,smart driving has become one of the current popular research fields.Vehicle detection and ranging is an important part of the environment perception of smart cars,and is also the basis for safe driving of smart cars.The traditional vision-based vehicle detection and geometric transformation-based ranging methods still cannot meet the requirements of intelligent vehicles in terms of detection speed and accuracy.To solve this problem,it is important to apply convolutional neural networks to vehicle detection and ranging and to study vehicle detection and ranging algorithms with high detection accuracy and high speed.The first detailed derivation of the conversion relationship between the coordinate systems involved in the process of converting the coordinates of points in the image to the coordinates of points in the 3D world,and the calibration and distortion correction of the camera used in this paper using the Zhang Zhengyou calibration method,the experimental results show that the calibration results of this paper are accurate and lay the foundation for the subsequent vehicle detection and ranging.For the vehicle detection task,the dataset used in this paper is established and manually labeled,and the principle of YOLOv4 detection algorithm is studied,and then the YOLOv4 algorithm is improved to improve the detection accuracy of the algorithm without losing speed.On this basis,the fused Cam Shift and improved YOLOv4 algorithms are proposed to improve the detection speed and real-time performance of the algorithm.The experimental results show that the fused Cam Shift and improved YOLOv4 algorithms proposed in this paper are improved over the YOLOv4 algorithm in terms of detection accuracy and detection speed.In order to address the shortcomings of the inherited geometric model-based ranging method,which requires many calibration parameters and strong geometric constraints,this paper proposes a vehicle ranging algorithm based on the regression of the bottom edge of the vehicle,using the data from calibration experiments and regression theory to obtain the regression model and propose the ranging formula.Finally,the fused Cam Shift and improved YOLOv4 algorithms are combined with the vehicle distance measurement algorithm based on the vehicle bottom edge regression,and the road real-world test hardware and software platform is built and then tested.