There are various types of surface defects in new energy vehicle components, and relying on manual inspection has problems such as false or missed inspections. In order to improve the accuracy of surface defects in new energy vehicle components, a surface defect detection method for new energy vehicle components based on machine vision technology is proposed. Combining Ridegelet transform and wavelet transform, denoising is performed on surface defect images of new energy vehicle components collected based on machine vision technology without damaging image details. MSR algorithm is introduced to realize fast image enhancement through adaptive calculation of information entropy proportion weight. The mathematical morphology calculation method is used to extract the edge of the main body of defects in the image of new energy vehicle parts, and then the surface defects of new energy vehicle parts are detected. The experimental results show that the proposed method has a detection time of less than 10 ms and accurate defect location analysis. In the face of surface defects in multiple types of new energy vehicle components, it can achieve accurate identification and improve the defect detection efficiency of new energy vehicle components.