The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniques

Abstract

Based on 3D images, this study aims to explore automatic segmentation and enhancement methods for airfield runway surface cracks. Firstly, a typical 2D Gaussian filter is used to remove noise from the road surface data. Then, Steerable Matched Filter (SMFB) is introduced to extract crack features. By constructing a set of 52 SMFB filters with different parameters, we are able to accurately capture cracks with different directions and sizes. After that, Tensor Voting (TV) technique is introduced to further enhance the continuity of the cracks. With this method, we are able to detect and segment the cracks in the airfield runway surface for a more accurate and comprehensive analysis. The experimental results show that the proposed method performs well in crack detection and segmentation, providing strong support for airport pavement maintenance and management.