Defect Detection of Composite Materials Using Channel Attention and Convolutional Autoencoder

As an effective non-destructive technology, infrared thermography is often used to detect inner defects in composites. However, non-homogeneous background in the thermal images reduces image quality inevitably. A squeeze and excitation convolutional autoencoder thermography (SE-CAT) technique is introduced for detecting underlying defects. Specifically, the channel attention mechanism assigns weights to different convolutional channels to highlight certain prominent features. As a result, the defect information can be extracted in a targeted manner using the convolutional autoencoder module. Finally, an experiment of carbon fiber reinforced composite materials shows good defect detection performance of the proposed SE-CAT method.