Rock mass exposure fracture detection through 2D close-range images using image processing techniques: a review

Discontinuity in rock masses, such as joints and faults, is crucial for geological assessments and stability in geoscience, geoengineering, and mining. Accurately detecting these fractures is essential for understanding geological structures. Advances in imaging and image processing have improved fracture detection, with 2D imagery being a practical and cost-effective tool due to its ease of acquisition and ability to utilize past datasets for continuity in analysis. Despite the undeniable advantages of 3D data, 2D imagery remains practical and is often utilized, even within 3D databases, especially as a solution for dimensionality reduction or simplification in various analyses. This review examines methodologies for detecting rock mass fractures using 2D close-range photographs, discussing semi-automatic and automated techniques, including machine learning and image processing approaches. It outlines the strengths and weaknesses of each method, highlights the integration of contemporary machine learning algorithms like Convolutional Neural Networks (CNNs) with traditional techniques, and explores potential future developments in automated fracture detection systems. By analyzing 35 relevant studies, the review categorizes methodologies by image acquisition points, image databases, and detection techniques, providing a comprehensive understanding of the current state, challenges, and future opportunities in rock mass fracture detection.