Edge detection is the main basis for separating the target object and background of the image. The edges of the image are determined by detecting a set of pixel points whose gray level changes significantly on the image. At present, most of the methods of edge detection apply two algorithms to improve the quality of edge detection. In this paper, GAUSS-filter, SOBEL, NMS and OTSU algorithms are adopted and implemented on the FPGA to perform image edge detection. The original collected color image is converted into a grayscale image, and the image is firstly smoothed by GAUSS-filter to eliminate interfering noise. The SOBEL operator is used to calculate the image gradient value, then the NMS algorithm is applied to process the grayscale, and pick out a set of the grayscale pixels with the largest gradient value of the contour edge. Finally, the OTSU algorithm is used to adaptively determine the image threshold for edge detection. This method can not only describe the edge of the image, but also refine the edge. The experimental results show that the image has a good detection effect on different images. The method proposed in this paper that has been implemented on FPGA of the image edge detection has the characteristics of accurate, real-time, high-speed, embedded, etc., and can be applied to many fields.