Deep learning for the detection and classification of adhesion defects in antique plaster layers

This paper aims is to show an automated intelligent measurement system for the detection of adhesion defects between architectural antique plaster layers. The method emulates the traditional conservators' procedure based on acoustical perturbations, auscultation, detection and classification. The system makes use of a hardware device, known in literature as PICUS, for the generation and acquisition of acoustic signals, while the processing of the acquired signals is handled by a deep learning (DL) architecture designed ad hoc. After a brief description of the PICUS system and the acoustic data acquisition procedure, the whole architecture of the DL system is carefully described. The proposed method has been validated by a significant case study. The system shows an accuracy of up to 82% (± 2%) in multi-class classification and up to 99% (± 1%) in binary classification. In particular, the obtained results suggest a satisfactory precision in the detection of areas where stabilization is necessary.