Enhancing Defect Detection on Surfaces Using Transfer Learning and Acoustic Non-Destructive Testing

Debonding, especially in plastic materials, refers to the separation occurring at the interface
within a bonded structure composed of two or more polymeric layers. Due to the great
heterogeneity of materials and layering configurations, highly specialized expertise is
often required to detect the presence and extent of such defects. This study presents
a novel approach that leverages transfer learning techniques to improve the detection
of debonding defects across different surface types using PICUS, an acoustic diagnostic
device developed at Roma Tre University for the assessment of defects in heritage wall
paintings. Our method leverages a pre-trained deep learning model, adapting it to new
material conditions. We designed a planar test object embedded with controlled subsurface
cavities to simulate the presence of defects of adhesion and air among the layers. This was
rigorously evaluated using non-destructive testing using PICUS, augmented by artificial
intelligence (AI). A convolutional neural network (CNN), initially trained on this mock-up,
was then fine-tuned via transfer learning on a second test object with distinct geometry
and material characteristics. This strategic adaptation to varying physical and acoustic
properties led to a significant improvement in classification precision of defect class, from
88% to 95%, demonstrating the effectiveness of transfer learning for robust cross-domain
defect detection in challenging diagnostic applications.