用关键点定位解决微裂纹检测中的数据不平衡问题
Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization
- 通过预测裂纹四个关键点坐标实现精准定位
- 小裂纹平均IoU达0.511，大裂纹达0.631
- 适用于非视觉检测场景，适合结构健康监测研究者
内部裂纹检测在结构健康监测中备受关注。聚焦于结构数据集中的裂纹检测，研究表明深度学习（DL）方法可有效分析与微尺度裂纹相互作用的地震波场，这些裂纹超出传统视觉检查的分辨率。本研究探索了一种基于深度学习的关键点检测新应用，通过预测定义裂纹边界区域的四个关键点坐标来定位裂纹。该方法不仅开辟了非视觉应用的新研究方向，还有效缓解了以往深度学习模型面临的数据不平衡问题——即模型易偏向多数类（无裂纹区域）。研究采用Inception块等主流深度学习技术进行验证。模型在微尺度裂纹检测中表现出更低的整体损失，实际与预测裂纹位置间的平均偏差减小。所有微裂纹（大于0.00微米）的平均交并比（IoU）为0.511，大于4微米的裂纹平均IoU为0.631。
原文摘要 · Abstract (English)
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).
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