Design And Implementation Of The Image Edge Detection System Based On FPGA

Edge detection technology is an important branch of image segmentation techniques,and it can provide effective characteristic information for target extraction technology,image analysis technology.With the increasing demands improved,at present how to improve the calculation speed and inspection accuracy is the outstanding problem of edge detection system.With the development of manufacturing process,FPGA has gradually become the effective tool in designing image processing system with high density,large capacity,on-chip memory and other characteristics.According to different requirements,the paper designs edge detection algorithm based on the first derivative and second derivative,its core operation device is FPGA.For the image which contains Gaussian noise and is not sensitive to the background edge character,the paper designs an improved edge detection algorithm based on the LoG operator.First of all,the paper determines the direction of the gradient by calculating the first-order partial derivatives of different directions,then determines the zero crossing point location by the LoG operator value of gradient direction,lastly filters and connects breakpoint in the zero crossing point image gained according to the zero crossing points of the adjacent points in the gradient of the normal vector.To facilitate the engineering application,the paper designs the hardware algorithm based on the FPGA chip.The experiment result shows that this algorithm can accurately perform the edge of the original image.For the image,its edge was intensive and threshold was not manually input,an adaptive edge detection method based on improved NMS(Non-Maximum Suppression)was proposed in this paper.In the method,the gradient image was computed by four directional Sobel operators.Then,the gradient image was processed by using NMS method.By defining a power map function,the elements values of gradient image histogram were mapped into a wider value range.By calculating maximal between-class variance according to the mapped histogram,the corresponding threshold was obtained as adaptive threshold value in edge detection.Finally,to convenient in engineering application,the proposed method was realized in FPGA(Field Programmable Gate Array).The experiment results demonstrated the proposed method was effective in edge detection,and suitable for real-time application.