Defect classification in shearography images using convolutional neural networks

High subjectivity, lack of attention and fatigue are factors inherent to human analysis in inspection activities such as shearography, a non-destructive optical method. In order to minimize the probability of human error, a study was conducted in which a binary classification from 256 shearography test samples obtained from pipes repaired with glass fiber patches was performed. The dataset was split into major and minor defects and used to train two convolutional neural networks architectures, - a specific artificial neural network well known for its application on image classification. Architecture A achieved a maximum accuracy of 73% on major defect detection, while architecture B, slightly more complex, led to better results. Posterior studies on architecture B led to the conclusion that a combination of double layer filters and dropout layers are the best setup for this type of classification problem. It is possible that other architectures might lead to better results, but no grid search was performed to confirm this assumption. An accuracy of 79% was achieved with Architecture B, therefore is reasonable to say that convolutional neural networks are able to learn from parameters which are difficult to correctly process, such as the fringe patterns obtained from shearography test samples.