A sub-pixel edge detection method based on a tanh function model is proposed to address the problem of the difficult balance among real-time performance, accuracy, and robustness of traditional sub-pixel edge detection algorithms. First, an edge function model is established, and the approximate value and error of the constant term of the function equation corresponding to each pixel edge are calculated. Combined with the least-squares method, the overall functional model error is then minimized to obtain more accurate edge parameters. Experimental results based on both standard and actual images show that the proposed algorithm has advantages such as fast speed, strong robustness, and high detection accuracy and can meet the requirements of real-time detection using better evaluation indicators than other traditional algorithms.