Online metallic surface defect detection using deep learning

Abstract

Across a range of manufacturing contexts, automated quality control has been gaining significant attention because it offers competitive advantages such as cost reduction, high accuracy in defect detection and system stability over time. Although computer vision has been historically the most commonly applied method in this context, novel approaches such as deep learning have recently become more frequent and are used in cases where traditional methods cannot be applied. Because of the surface texture and curvature of many metallic parts, detection of defects such as scratches, cracks and dents can be challenging for traditional computer vision methods. In this study, an image acquisition system supported by a special lighting device that provides processable images from an extremely reflective cylindrical metallic surface has been developed. Multiple images obtained from a single lateral line of the surface, which is rotated at a specified speed, are combined using photometric stereo and given as input to a convolutional neural network that is employed to classify defective and non-defective samples. The results obtained from this method are close to 98.5% accurate.