Research On Low Illumination Video Acquisition And Edge Detection Algorithm Based On FPGA

FPGA is suitable for fast image processing because of its high-speed parallelism and pipeline data processing.In the application of image processing,the key of image is the information contained in the edge features.Edge detection is very important in target tracking,target recognition,supervised recognition under deep learning and machine vision detection.In the low illumination environment,the edge information of the image is not obvious,so it is very important to accurately detect the edge in the low illumination environment.Aiming at the problem of low accuracy of edge detection caused by image quality degradation in low illumination environm ent,an adaptive edge detection method based on gradient difference is proposed.Canny operator is selected as the edge detection operator and improved.A bilateral filter is used to replace the traditional Gaussian filter,and gradient calculation templates in 45° and 135° directions are added to highlight the edge.In view of the fact that canny threshold selection is not adaptive,Otsu algorithm is improved based on gradient difference to obtain adaptive threshold for segmentation.Finally,recursive boundary tracking method is used to connect the edges.On this basis,a real-time edge detection system is designed and implemented based on FPGA.The OV5640 camera is configured to collect image data.In order to improve the image quality in low illumination environment,the parameters of white balance and gamma correction are adjusted to adjust the image quality.Four direction Laplacian sharpening is added to the image enhancement to highlight the edge information,and the improved Canny algorithm is hard coded Finally,VGA is used to display the im age and detect the edge information.In this paper,SDRAM DDR3 chip is used to cache image data in the system to improve the robustness and data throughput of the system.SNR(signal-to-noise ratio)and the ratio of C/A and C/B are used to evaluate the edge detection effect.The test results show that compared with the traditional Canny algorithm,the improved Canny algorithm has better edge detection effect in low illumination environment,with clear and continuous edges.The adaptive threshold judgment time is only one tenth of the original,which is more suitable for real-time operation.The whole real-time edge system is built on the atrix-7 development board,and the experimental platform is vivado In January,2019,the real-time edge detection effect is evaluated by real-time processing with Sobel and traditional Canny algorithm.The test results show that the edge noise detected by this system is less,the weak edge extraction effect is good,and the edge is complete and continuous.The real-time processing speed of this paper can reach 60 frames per second,and the real-time performance is good.