MicroCrackAttentionNeXt: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks through Feature Visualization.

Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Sven Tomforde

Keywords: manifold discovery and analysis, Feature Visualisation, structural health monitoring, Attention mechanism, wave field data, micro scale cracks, Loss functions

TL;DR: This paper presents a deep learning approach, for detecting microcracks in materials and explores the effects of different activation and loss functions. It enables interpretability by utilizing Manifold Discovery Analysis for feature visualization.

Abstract:

Micro Crack detection using deep neural networks(DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. However, these high dimensional spatio-temporal crack data are limited, moreover these dataset have large dimension in the temporal domain. The dataset exhibits a pronounced class imbalance, with crack pixels accounting for an average of only 5% of the total pixels per sample. This severe imbalance presents a challenge for deep learning models when dealing with various microscale cracks, as the network tends to favor the majority class, often resulting in reduced detection accuracy. This study proposes an asymmetric encoder–decoder network with Adaptive Feature Reutilization Block for micro-crack detection. The impact of various activation and loss functions were examined through feature space visualisation using manifold discovery and analysis (MDA) algorithm. The optimized architecture and training methodology achieved an accuracy of 87.74%.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 13569