Characterizing fatigue micro-cracks is crucial for understanding the mechanisms and behaviors of material damage. In-situ fatigue testing is an essential method for observing the evolution of fatigue micro-cracks; however, the process often requires significant time, making the measurement of micro-cracks a tedious task. This paper introduces a multi-task deep learning framework called MT-CrackNet, which enables automatic detection and quantification of in-situ fatigue micro-cracks. The framework is capable of recognizing or segmenting multiple tasks such as micro-cracks, text, and scales simultaneously, and its effectiveness is not limited by the magnification of in-situ images. By integrating attention mechanisms and multi-scale strategies, the model enhances its ability to handle long-range dependencies and preserve detail information, accurately identifying and measuring the length of micro-cracks. The effectiveness of the proposed MT-CrackNet is validated through three in-situ fatigue micro-crack propagation experiments.

中文翻译：

---

MT-CrackNet：用于自动原位疲劳微裂纹检测和量化的多任务深度学习框架


表征疲劳微裂纹对于了解材料损伤的机制和行为至关重要。原位疲劳测试是观察疲劳微裂纹演变的重要方法;然而，该过程通常需要大量时间，这使得微裂纹的测量成为一项繁琐的任务。本文介绍了一种名为 MT-CrackNet 的多任务深度学习框架，该框架能够自动检测和量化原位疲劳微裂纹。该框架能够同时识别或分割微裂纹、文本和比例等多项任务，其有效性不受原位图像放大的限制。通过集成注意力机制和多尺度策略，该模型增强了其处理长期依赖关系和保留细节信息的能力，从而准确识别和测量微裂纹的长度。通过三次原位疲劳微裂纹扩展实验验证了所提出的 MT-CrackNet 的有效性。