Abstract: The existing line segment extraction algorithm extracts many meaningless lines from blurred areas such as the sky, shadow, glass and floor in the image. To solve this problem, a method for line segment extraction algorithm optimization based on Shi-Tomasi corner detector is proposed, which is called ST-Lines. It firstly uses a classic line segment extraction algorithm to extract lines. Then corners are extracted with the Shi-Tomasi corner detector and non-maximum suppression is applied to them with a sliding window. Finally, it verifies the meaning of each line segment and removes as many meaningless line segments as possible to optimize the line segment extraction algorithm based on the length of the line segment, the distribution of corners in the circle region of endpoints, and the K-nearest neighbor algorithm. The YorkUrban line segment dataset is used to compare ST-Lines algorithm and the original algorithm. The experimental results show that ST-Lines algorithm can improve the accuracy, F-score and the averageline length and reduce the average line number.