A study on defect detection in X-ray image castings based on unsupervised learning
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In this paper, we propose a deep-learning-based defect detection system for the non-destructive quality inspection of castings based on X-ray images. Our system comprises a defect classification network and a defect search network and achieves high classifi-cation performance with limited data by minimizing the overfitting for one type of object. The proposed defect classification network determines whether the acquired X-ray image is Defect by using a convolution neural network and outputs the defect probability through softmax. Compared to binarized defect classification or defect location tracking, this method of outputting the defect proba-bility does not require a separate reworking of the training dataset, because the data labeling is the same as the existing quality evalu-ation task. In addition, to detect the location of the defect causing the defect classification, our proposed defect search network estimates the region where the defect is likely to exist through a Grad-CAM based on the feature map of the classification network. The proposed network then determines the ROI around each peak of the estimated regions and detects the exact shape and location of the defect through boundary detection. It is, therefore, possible to minimize quality control costs through a precise quality analysis of each casting product by simultaneously detecting small defects that are easy to ignore because large defects are found in the image. To verify the validity of this study, an experiment was conducted by constructing a dataset of actual cast products, and the proposed detection model achieved an accuracy of 90%. In addition, by comparing the fully connected network and the SVM-based model, the model improved by about 20%, demonstrating that it is possible to detect defects without labeling defect locations.
Abstract
In this paper, we propose a deep-learning-based defect detection system for the non-destructive quality inspection of castings based on X-ray images. Our system comprises a defect classification network and a defect search network and achieves high classifi-cation performance with limited data by minimizing the overfitting for one type of object. The proposed defect classification network determines whether the acquired X-ray image is Defect by using a convolution neural network and outputs the defect probability through softmax. Compared to binarized defect classification or defect location tracking, this method of outputting the defect proba-bility does not require a separate reworking of the training dataset, because the data labeling is the same as the existing quality evalu-ation task. In addition, to detect the location of the defect causing the defect classification, our proposed defect search network estimates the region where the defect is likely to exist through a Grad-CAM based on the feature map of the classification network. The proposed network then determines the ROI around each peak of the estimated regions and detects the exact shape and location of the defect through boundary detection. It is, therefore, possible to minimize quality control costs through a precise quality analysis of each casting product by simultaneously detecting small defects that are easy to ignore because large defects are found in the image. To verify the validity of this study, an experiment was conducted by constructing a dataset of actual cast products, and the proposed detection model achieved an accuracy of 90%. In addition, by comparing the fully connected network and the SVM-based model, the model improved by about 20%, demonstrating that it is possible to detect defects without labeling defect locations.
참고문헌 (19)
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