Photoacoustic signal to image based convolutional neural network for defect detection.

In this paper, we propose a novel photoacoustic (PA) signal to image conversion based convolutional neural network (CNN) model for defect detection in materials. A low-cost computer aided PA triggering and acquisition device has been developed, and then, PA signals are stored for four types of defected and intact materials. Variational mode decomposition is applied to the dataset to extract intrinsic mode functions to convert PA signals to images as the first step of the feature extraction, and then, a lightweight CNN architecture is trained and tested using converted grayscale PA images to detect as defected or intact material. The proposed model is performed on the PA signals of aluminum, iron, wood, and plastic depending on the within-class and all-class evaluation strategies. The mean accuracy levels of 0.977 (up to 1.0) for within-class (material dependent) and 0.942 (up to 0.955) for all-class (material independent) are yielded.