Aluminum profiles are an important application material in the construction and machinery industries, and their industry-wide output and consumption are increasing year by year worldwide. During the production process of aluminum profiles, due to material characteristics and processing technology, surface defects are inevitable, which seriously affects the reliability, safety and processability of aluminum profiles.
In actual production, accurate and rapid identification of surface defects of aluminum profiles is crucial to ensuring the quality of aluminum profiles. Traditional aluminum profile surface defect identification methods include eddy current testing, ultrasonic guided wave testing and infrared testing. The identification costs are high, the equipment is complex, and it is difficult to visualize the defect identification process. As a non-contact online automatic inspection technology, machine vision inspection has the characteristics of non-contact, high safety, high identification efficiency and long working time. It is an effective means to achieve accurate and rapid identification of surface defects.