Rock fracture identification algorithm based on the confidence score and non-maximum suppression

The information of fractures in rock mass is an essential indicator to evaluate the quality of rock mass. It is precise and efficient to obtain information of fractures by computer vision (CV)-based fracture detection technologies. However, rock masses in practical engineering always exhibit complex fractures with messy edges, shadows, and uneven rock surfaces. These factors negatively impact the effectiveness of fracture detection algorithms. To address such issues, this paper proposes a confidence score-based rock fracture detection algorithm that can effectively identify fractures in complex situations. The image with fractures is traversed to acquire candidate points at first. We proposed a method to evaluate the confidence scores of these candidate points according to the values of Hessian matrix eigenvalues, the degree of symmetry, and the gray-scale value. The candidate points are further filtered through threshold restriction and non-maximum suppression. Furthermore, the complete centerlines of the fractures are obtained by the connection of the candidate points and the connection of discontinuous centerlines. Finally, pseudo-fractures and noise are eliminated to get complete fractures. Experimental results show that the newly proposed algorithm can accurately identify fractures in complex situations. The algorithm avoids interference and noise, which achieves more precise results than other conventional fracture detection algorithms, is an effective method to identify fractures in rocky geological engineering and can also serve as a guide for other relevant fracture identification algorithm in rock mass.