Detecting fatigue cracks in underwater structures presents unique challenges due to the complexities of the underwater environment. With the advancements in deep learning and computer vision techniques, inspection methods are transitioning towards automated machine learning–based approaches. However, the inherent uncertainties of the underwater realm pose difficulties in effectively training and utilizing these models. To address this, the research introduces a framework employing a graphics-based digital twin, derived from a finite element model, to generate synthetic images under diverse conditions. With a deep learning–based crack detection, this approach facilitates a quantitative evaluation of the impact of specific environmental factors on crack detection probabilities. By comprehending these impacts, more effective inspection strategies for large underwater structures can be developed. The utility of this framework is demonstrated through a case study on a miter gate, demonstrating its potential to improve crack detection by considering the dynamic and uncertain environmental conditions in real-world contexts.