Surface texture defect recognition method of machined parts based on machine vision

In order to improve the recognition rate of surface texture defects of machined parts, a machine vision-based method for identifying surface texture defects of machined parts is proposed. By analyzing the characteristics of the detection content of small parts, the image is filtered, the improved edge detection algorithm is used to accurately extract the edge position of the surface, the image is divided into different regions by the edge, the candidate defects are screened, and the BP network is finally used Algorithms classify and mark defects. The experimental results show that the method can stably recognize and detect the surface defects of small parts, the defect recognition rate is up to 97.05%, and the detection speed is also fast, reaching the expected detection target.