Detection and Classification of Aviation Cable Insulation Defects Using Digital Holography and Deep Learning

Abstract

The insulation of aviation cables is critical to aircraft safety but is vulnerable to defects such as cracks, ruptures, slices, and swelling. Reliable nondestructive testing (NDT) of these defects is challenging due to environmental interference, noise, and the limitations of existing inspection techniques. This work presents a novel NDT approach integrating reflective digital in-line holography with a Combined Anisotropic Total Variation (CATV) reconstruction algorithm and an Xception-based deep transfer learning model. The CATV reconstruction suppresses twin-image artifacts and preserves structural detail, enabling the generation of a phase-map dataset of multiple defect types. Using this dataset, the Xception-based classifier achieved 98% accuracy, surpassing state-of-the-art approaches. The contributions of this work are: (i) using CATV-based reconstruction for reflective holography of aviation cables, (ii) creating a phase-map dataset of insulation defects, and (iii) demonstrating the feasibility of a high-precision, non-contact inspection method for aviation safety applications.