Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization

TL;DR

This paper introduces a deep learning approach using key point localization to detect micro-scale cracks in structural datasets, effectively handling imbalanced data and improving localization accuracy over traditional methods.

Contribution

It presents a novel DL-based key point detection method for micro-crack localization that mitigates data imbalance issues and enhances detection accuracy.

Findings

Lower average deviation between actual and predicted crack locations.

Average IoU of 0.511 for all micro cracks.

IoU of 0.631 for larger micro cracks.

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 analyze 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 Intersection over Union (IoU) being 0.511 for all micro cracks (greater than 0.00 micrometers) and 0.631 for larger micro cracks (greater than 4 micrometers).