Abstract: Rail image captured by machine includes rail surface area and interferenc zone (track, gravel, weeds, etc.). It is difficult and time-consuming to detect defective targets from complex track images. Therefore, It can save a lot of time to extract the rail area first, and then detect and identify the defects in the extracted rail area. The traditional rail surface region (abbreviated as rail surface region) extraction algorithm mostly determines the rail surface boundary manually, the application range of the algorithm is narrow, poor adaptability, and sensitive to light. In order to solve the above problems, the fast extraction method of rail area is studied and an algorithm for quickly extracting rail surface region based on HSV space is proposed in the thesis. Firstly, the acquired RGB image is transformed into HSV space, and its S component image is extracted, through this transformation to overcome the interference caused by changes in lighting conditions; Secondly, a gray projection curve is drawn in the S component image; Then use the midpoint of the image as an axis to divide the curve into the left and right sides, and find the maximum and minor values of the left and right column curves respectively. Finally, the boundary of the rail surface region is automatically determined based on the relationship between the maximum value and the second largest value. The simulation results show that the proposed algorithm can accurately and quickly extract the rail surface region and avoid manually determining the boundary of the rail surface region. The generalization ability of the algorithm is better and the extraction accuracy is higher (I_oU is up to 0.92). the average extraction accuracy is 93.87%, and the extraction time is 0.046 s, significantly lower than the traditional method, it lays a foundation for real-time automatic detection of rail track.