High strength and highly reflective metal sheets are widely applied in industry; industrial requirements for defect detection are extremely demanding, particularly in the aviation and automotive industries. Classifying and recognizing surface defects on steel plate surfaces is highly challenging. Currently, defect detection is still inspected visually by personnel. However, given the high temperatures at inspection sites and the high risks in the operating environments, machine vision inspection systems are expected to replace manual inspection processes eventually. Therefore, this study developed an automated defect detection system that reduces the high reflectivity of examined objects. The light sources emitted light rays to the rays diffused and reflected multiple times inside the hemispherical cover to produce uniform illumination. Subsequently, image processing was conducted to highlight defect features on the stainless-steel plates. Relatively favorable light source positions were identified, which reduced the difficulty of class identification, the omission rate in defect detection to be decreased, and frequently encountered reflection problems in the automated optical inspection of metallic products to be overcome.

No abstract