MT-CrackNet:A multi-task deep learning framework for automatic in-situ fatigue micro-crack detection and quantification

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.