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.

中文翻译：

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基于改进RANSAC-SURF算法的车辆座椅检测

为了检测汽车座椅的类型和弹簧钩的缺失部位，本文提出了一种改进的RANSAC-SURF方法。首先，图像由高斯滤波器过滤。其次，使用改进的 RANSAC-SURF 算法来检测车辆座椅的类型。提取汽车座椅的特征点。根据改进的RANSAC-SURF算法匹配特征点。第三，通过透视变换的方法校正车辆座椅的图像失真。根据摄像头采集到的图像与正常安装图像的灰度差绝对值判断座椅弹簧钩是否缺失。实验结果表明，高斯滤波器的 MSE 在 5*5模板为19.0753，PSNR为35.3261，优于均值滤波器和中值滤波器。在改进的RANSAC-SURF匹配算法中，特征点的总匹配对数和相交点的数量分别为188和18。