The automobile paint film may shield the body from corrosion caused by the outside environment, but because of the effect of the manufacturing process, there will be a variety of flaws when the paint is applied. Therefore, the progress of automated automotive production depends on the identification of small flaws in paint film. Based on the actual needs of automotive paint film inspection, this paper proposes a visual image-based detection method for small defects in car paint film. This method first superimposes multiple frames of automobile paint film images to suppress noise; then uses two-dimensional wavelets to extract image trend items and characteristics of minor defects; finally, through the local adaptive threshold and the optimized segmentation coefficient of field curvature, the binary segmentation of defects is realized. The experimental results show that the method in this paper can effectively detect defects larger than 0.1mm on large-area images, and the recall rate of small defects reaches 97.6%, and the false detection rate is 1.3%, which can achieve the expected detection results.