Deep Learnıng-Based Sustaınable Subsurface Anomalıes Detectıon In Barker-Coded Thermal Wave Imagıng

Abstract

Deep learning-based sustainable subsurface anomaly detection is the perceiving of thermographic research. Subsurface detection of an anomaly in various materials using deep learning increases reliability. This article aims to describe a method that uses thermal wave imaging to identify subsurface anomalies in materials. The proposed method is based on the experiments that were carried out with different kinds of samples and have been compared to other modern techniques for detecting subsurface anomalies. Subsurface anomalies visualized using the proposed deep learning method give better visualization, and the results were compared to that of contemporary approaches. In addition, region-based active contour segmentation-based detection is also proposed for the GFRP sample.