Vehicle Seat Detection Based on Improved RANSAC-SURF Algorithm

摘要

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat's spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 * 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm.