In the current intelligent manufacturing process, the requirements for zero scratch quality of precision products and instrument surfaces are constantly improving. The scratch detection method based on machine vision shows important research significance because of its non-destructive and high-precision characteristics. This paper summarizes the development status of scratch detection technology based on machine vision and divides the current mainstream scratch detection methods into manual design features and deep learning methods. The scratch detection methods based on manual design features include gray distribution statistics, transform domain, and high- and low-dimensional space mapping methods. The scratch detection methods based on deep learning include supervised and unsupervised learning methods. The advantages of each method are summarized, disadvantages and application scenarios are described, and development trends of scratch detection technology based on machine vision are expounded.