Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization
Keywords: Micro-scale crack detection, Imbalanced datasets, Key point localization, Squeeze-and-excite blocks, Deep learning in structural health monitoring, Wide convolutional networks, Structural defect localization, Seismic wave analysis, Bounding box regression
TL;DR: The study focuses on applying deep learning for detecting micro-scale cracks in structural materials using key point localization. The model predicts four key points to define crack boundaries, improving detection on imbalanced datasets
Abstract: Internal crack detection has been a subject of focus in structural health monitoring. By focusing on crack detection in structural datasets, it is demonstrated that deep learning (DL) methods can effectively analyse seismic wave fields interacting with micro-scale cracks, which are beyond the resolution of conventional visual inspection.
This work explores a novel application of DL based key point detection technique, where cracks are localized by predicting the coordinates of four key points that define a bounding region of the crack.
The study not only opens new research directions for non-visual applications but also effectively mitigates the impact of imbalanced data which poses a challenge for previous DL models, as it can be biased toward predicting the majority class (non-crack regions). Popular DL techniques, such as the Inception blocks are used and investigated.
The model shows an overall reduction in loss when applied to micro-scale crack detection and is reflected in the lower average deviation between the location of actual and predicted cracks, with an average IOU being 0.511 for all micro cracks (> 0.00 µm) and 0.631 for larger micro cracks (> 4 µm).
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 13527
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