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Quality control and defect detection are major challenges in industrial environments. The localization of these defects is of crucial importance, as they can affect the performance of manufactured products and even the safety of people. Defect detection methods based on visual sensors and image processing are nowadays the most common approaches. To detect subsurface defects it is common to combine active thermography and deep learning. In this paper, PCT (Principal Components Thermography) is employed to enhance the SNR (signal-to-noise ratio), leading to a significant improvement in the results. Recent developments in this field apply deep learning models for semantic segmentation and object detection. However, the quality of the predictions is not enough to ensure that all quality controls are met. In this paper, a combination of semantic segmentation and object detection is proposed to increase the reliability of predictions. To carry out this combination, a working methodology and two new ensembling strategies are proposed. After comparing the results of the proposed combination with the results of using only semantic segmentation methods in a real industrial scenario, where carbon fiber sheets are used, it is found that the proposal improves the segmentation metrics by a 5% to a 24%. Thus the reliability of the predictions is improved.

Improved detection of subsurface defects through active thermography and ensembling techniques