Automated defect detection on inductive thermography images using supervised and semi-supervised Deep Learning methods

Abstract

Inductive infrared thermography has been proven as an interesting solution for the inspection of surface defects. To automate the inspection, defect detection methods based on convolutional neural network proved their efficiency for complex detection tasks compared to traditional methods. Both supervised and semi-supervised learning approaches have been proposed for the inspection task. While the supervised approach remains the most common one, it requires images of both defective and non-defective parts during the training phase. Unfortunately, in many industries where the scrap rate is low, acquiring images of defective parts is difficult and requires time which can delay the deployment of such solutions. This paper compares these two learning approaches by illustrating the advantages and disadvantages of each approach from an industrial point of view. In conclusion, we describe an inspection deployment strategy, which combines the two approaches to ensure robust inspection with rapid deployment.