How AI revolutionized the visual inspection of metal surfaces

How AI revolutionized the visual inspection of metal surfaces. Researchers found a new way to generate images of metal surface defects.

The 4.0^th industry revolution aims to develop smart factories which employ the Internet of Things (IoT) as well as Artificial Intelligence (AI). In the metal industry, predicting product quality in real-time and giving specific feedback on the type, size, shape, and location of each defect are crucial tasks, which can be best achieved with the help of an intelligent machine that takes care of automatic defect detection and data quantification. Defect recognition and classification can be performed by supervised and even unsupervised deep learning techniques.

Dr. Jong Pil Yun from Korea Institute of Industrial Technology, Republic of Korea, and his colleagues have recently researched the applicability of machine vision in detecting metal surface defects. As mentioned in their paper published in the journal of Manufacturing Systems, though Deep Convolutional Neural Networks (DCNNs) are being successfully used in manufacturing industries for defect recognition and classification, they suffer from the lack of large training datasets due to the difficulty of gathering such data. Using small training datasets or poor-quality ones results in an overfitted classification model or poor generalization performance. To tackle this problem, data scientists turn to data augmentation techniques. Some conventional methods to generate image data through label-preserving linear transformations include translating, rotating, scaling, blurring, changing contrast, flipping, shear mapping, adding noise, sharpening, and so on. However, these techniques will only create image-level transformations through scale and depth and would do little to nothing in dividing boundaries between data manifolds. In addition, this kind of augmented data cannot enhance data distributions which are determined by higher-level features.

Yun et al. proposed a novel convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to address this issue. Indeed, a conditional CVAE algorithm can generate adequate data for each defect type in a single CVAE model to train the classification model. Then, by using DCNN models with high generalization performance they can classify all defects. To verify their model, they applied the trained model to images obtained from an actual metal production line and proved that the model works well.

“A defect inspection system that detects defects in real-time and classifies defect types, is one of the key technologies required for the implementation of the smart factory ”