| Self-driving cars can bring convenience to people’s lives.Real-time lane detection in the driving environment is the primary basic task for achieving automatic driving.At present,the research on lane detection is mostly based on CPU+GPU development platform.The platform is low in cost performance,high in power consumption and large in size,and is not suitable for use in vehicle scenes.In order to meet the requirements of real-time lane detection performance,power consumption and flexibility,this paper designs a lane detection system on the FPGA development platform,and finally realizes real-time lane detection.The main research contents of this paper are as follows:Lane line detection.This paper first enumerates and compares the commonly used implementation methods,including image processing and semantic segmentation.After comparing the real-time and computational complexity,the implementation scheme based on image processing algorithm is developed.Then the lane line detection is designed.Each module mainly includes threshold segmentation,inverse perspective transformation and lane line fitting.Finally,important information such as the driving area,the radius of curvature of the lane,the direction of the lane of the lane,and the direction and distance of the vehicle from the center of the lane are output,and the lane line detection is completed.Lane line type identification.This paper firstly proposes the necessity of lane line type recognition and the method of dynamic extraction of recognition region.Then it learns the Caffe deep learning framework and convolutional neural network algorithm,and learns the principle and implementation method of target recognition based on convolutional neural network algorithm.Research;then make the lane line dataset,and design and train the appropriate network structure on the PC according to the actual situation of the dataset and hardware platform resources;then complete the identification network construction and testing in the FPGA environment;finally change the network The parameter type,the insertion pipeline and other methods optimize the program design,so that the usage of the integrated hardware resources and the real-time performance of the system meet the actual requirements.Data path design.This paper collects data through OV7725 and inputs it into FPGA.Then it performs data pre-processing through data format conversion module and transmission interface conversion module.Then,through the lane line detection moduleand lane line type identification module,the radius of curvature of the lane,the direction of lane curvature and the direction of the lane are obtained.Important information such as the type of lane line,and finally output the test results to an external display.The design of the whole system is based on the ZYNQ7035 SoC development platform,and the lane line detection,lane line type identification,data path design and other work are completed on the platform,and the channel design is optimized according to the platform resources and characteristics,and finally the lane line is clear and the road background is realized.A single structured road for lane detection with speeds above 104 fps. |
