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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.

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.