An innovative deep learning-based method is introduced for detecting fine cracks in high-resolution images of concrete dam surfaces, addressing the urgent need for efficient and accurate maintenance of these vital infrastructures. Traditional manual inspection methods often fail to detect subtle cracks, leading to potential safety hazards and costly repairs. By leveraging advanced deep learning techniques, this study develops a model that automates the detection process, improving both precision and efficiency. High-resolution images are collected and meticulously annotated to create a comprehensive dataset, which is then used to train a deep learning model designed for crack identification. The model's performance is evaluated against a separate test dataset, demonstrating significant improvements in detection accuracy. This automated approach not only facilitates timely interventions but also contributes to enhanced monitoring strategies, ultimately ensuring the structural integrity and safety of concrete dams in the long term.