Defect detection by a deep learning approach with active IR thermography

Nowadays, non-destructive techniques (NDT) play a fundamental role in the production industry since early defects detection (EDD) can reduce possible costs and avoid catastrophic failures. Under these aspects, all methods for fast and reliable inspection deserve special attention. This paper proposes a method to detect manufacturing defects or other damage mechanisms without compromising the original condition of the material using active IR thermography and automatic semantic segmentation. The segmentation of defects in composite materials is achieved by using a deep learning algorithm on a high-variance dataset obtained performing lockin thermography under five different heat source configurations. Experimental results on specimens with known defects have demonstrated that the proposed methodology provides satisfying performances in automatic defect detection.