Real-time hollow defect detection in tiles using on-device tiny machine learning

Abstract

This study addresses the challenge of subsurface defect detection in floor tiles for quality control in residential construction. To overcome the limitations of traditional inspection methods and the complexities associated with existing artificial intelligence (AI)-based approaches, we have developed the AI Diagnostic Stick (AID-Stick), a novel tool designed to advance the field of tile defect detection. This innovative tool integrates an embedded machine-learning framework, leveraging convolutional neural networks (CNNs) and tiny machine learning (TinyML) techniques. The AID-Stick utilizes spectrogram, Mel-frequency cepstral coefficient (MFCC), and Mel filterbank energy (MFE) for real-time, on-microcontroller unit (MCU) diagnostics of auditory signals from tile tapping tests. Our methodology effectively utilizes these acoustic features in distinguishing between intact and subsurface hollow defective tiles. The study's findings, revealing a notable validation accuracy of 97% and a real-world accuracy of 81.25%, showcase a promising improvement over traditional methods. The AID-Stick's practicality, cost-effectiveness, and user-friendly design make it potentially beneficial for small-to-medium enterprises and economically constrained markets. Furthermore, this research opens avenues for future enhancements in embedded AI systems, with potential applications extending beyond the construction industry to other domains requiring non-destructive testing. This work not only contributes to the field of industrial quality control but also to the development of intelligent diagnostic tools, paving the way for future innovations in automated inspection technologies.