A statistical method for crack detection in 3D concrete images
This work addresses computational bottlenecks in crack detection for structural integrity analysis of materials, representing an incremental improvement.
The paper tackled the problem of high computational costs in crack segmentation for large-scale 3D CT images by proposing a streamlined pre-detection method, which was validated to enhance segmentation efficiency and reduce resource requirements in tests on semi-synthetic and real images.
In practical applications, effectively segmenting cracks in large-scale computed tomography (CT) images holds significant importance for understanding the structural integrity of materials. However, classical methods and Machine Learning algorithms often incur high computational costs when dealing with the substantial size of input images. Hence, a robust algorithm is needed to pre-detect crack regions, enabling focused analysis and reducing computational overhead. The proposed approach addresses this challenge by offering a streamlined method for identifying crack regions in CT images with high probability. By efficiently identifying areas of interest, our algorithm allows for a more focused examination of potential anomalies within the material structure. Through comprehensive testing on both semi-synthetic and real 3D CT images, we validate the efficiency of our approach in enhancing crack segmentation while reducing computational resource requirements.