As the obstacle detection technology has become an important part of ADAS, many kinds of obstacles are difficult to cover. Therefore, a method of detecting small obstacles during car parking based on panoramic vision system is proposed. First, the real-time pictures in four directions map to the panoramic view using perspective transformation and image mosaic. A detection model of the ground feature point is proposed, which can be accurately extracted and matched with the right angle intersection of the parking line. After obtaining the ground feature points in the two frames,self-vehicle motion estimate is calculated by the SVD decomposition method and the simulated current frame of the previous frame is obtained, and the dynamic background is eliminated. Finally, a detection method based on color segmentation is proposed to determine whether it is an obstacle part. To verify the feasibility of the algorithm, various small obstacles were placed in the parking for testing, with a total of 864 obstacles in the three video sequences, with an average true positive rate of 94. 7% and an average false alarm Rate of 7. 3%. The results show that the algorithm can detect small obstacles in parking with certain accuracy and robustness.