High-Accuracy Detection and Classification of Defect and Deformation of Metal Screw Head Achieved by Convolutional Neural Networks

Abstract

Nowadays, the industry requires automatic production for high-speed manufacturing. However, the products must also maintain high quality and reliability. An efficient inspection technique should be conducted for the improvement in the manufacturing quality. In order to achieve high inspection rate, optical inspection based on machine vision often raises the threshold of the judgment and this will worsen false detection. In this study, we propose a high-accuracy optical inspection system based on deep learning technology. Various defects in screw head are precisely detected and analyzed, which include surface damage, unprocessed, and stripped surfaces. An industrial camera and microscope system are employed to collect the raw images of metal screws with different defect types. The raw images of 3200 are utilized to train the designed convolutional neural networks. The experimental results indicate that the proposed system reaches a detection accuracy of 92.8% and the average detection speed is 0.03 second per image. In comparison with conventional machine vision methods, the proposed measurement system is more suitable for the inspection of industrial production line.