Image edge detection is a technique that extracts mutation information from images and is widely used in the fields of image processing and computer vision. The effectiveness of image edge detection directly affects the accuracy of subsequent region information extraction, target recognition, and pose measurement. Taking into account two factors: local extremum and gradient direction, and combining with the trend of image edge direction, a single-pixel edge tracking strategy was proposed for the edge detection problem of low contrast and edge blurred images. Compared with the widely used Canny algorithm, this tracking strategy did not require setting a global threshold, and its implementation was more concise and efficient. The extracted image edges were continuous, smooth, and complete, effectively reducing redundant pixels at the image edges, thereby improving the efficiency of subsequent image processing. Edge tracking direction had strong anti-interference ability and robustness. In order to reduce the deviation between the detected image edge and the real image edge, and improve the accuracy of image edge detection, the adjacent gray values of edge pixels were referred to, and the gradient distribution of edge pixels was used as the basis for sub-pixel localization of that pixel. Through experimental verification, the sub-pixel optimized image edge detection strategy can be used to detect images with blurred edges and low contrast. The detected image edges were complete, continuous, and smooth. This strategy effectively eliminated truncation errors introduced in program operations, improved the accuracy of image edge detection, which was suitable for high dynamic imaging scenes with a brightness range of 5~100000lx.