A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent

Cracks at the corners of casting billets have a small morphology and rough surfaces. Corner cracks are generally irregular, with a depth of about 0.2–5 mm and a width of about 0.5–3 mm. It is difficult to detect the depth of cracks and the three-dimensional morphological characteristics. The severity of cracks is hard to evaluate with traditional inspection methods. To effectively extract the topographic features of corner cracks, a multi-scale surface crack feature extraction algorithm, based on weighted adaptive gradient descent, was proposed. Firstly, the point cloud data of the corners of the billet were collected by the three-dimensional visual inspection platform. The point cloud neighborhood density was calculated using the k-nearest neighbor method; then the weighted covariance matrix was used to calculate the normal rate of change. Secondly, the adaptive attenuation rate, based on normal change, was fused with the density weight, which can calculate the Gaussian weight in regard to the neighborhood. Gaussian weights were used to obtain the gradient changes between point clouds to acquire the multi-scale morphological features of the crack. Finally, the interference caused by surface and boundary effects was eliminated by DBSCAN density clustering. The complete three-dimensional morphology characteristics of the crack were obtained. The experimental results reveal that the precision rate, recall rate, and F-value of the improved algorithm are 96.68%, 91.32%, and 93.92%, respectively, which are superior to the results from the RANSAC and other mainstream algorithms. The three-dimensional morphological characteristics of corner cracks can be effectively extracted using the improved algorithm, which provides a basis for judging the severity of the defect.