Automated Detection of Subsurface Defects Using Active Thermography and Deep Learning Object Detectors

Abstract

The need for improved quality control in industry makes object detection crucial. This work addresses the challenging problem of subsurface defect detections using a combination of active thermography and deep learning. The novel contribution of this work is to pose the problem as one of object detection rather than semantic segmentation or classification. The images used as input for the deep learning algorithms are three-channel color images obtained using principal component thermography (PCT). The use of these images improves the signal-to-noise ratio (SNR). A framework to label ground truths automatically is also created. The most widely used deep learning detector algorithms were evaluated, and the fifth version of you only look once (YOLO), i.e., YOLOv5, was selected because of its excellent average precision (AP) and its low inference time. The resulting combination of this algorithm and active thermography is effective and accurate in detecting subsurface defects.