Using the fuzzy integrals for the ensemble-based segmentation of asphalt cracks

Abstract

Detection and measurement of cracks in asphalt pavement is one of the important tasks in transport industry to determine the quality of the pavement and submit repair requirements. In recent years, computer vision algorithms have been increasingly used to automate the solution of this problem. Therefore, researchers are faced with the acute issue of improving the accuracy of segmentation algorithms, since the safety of people depends on the timely detection of defects on the road. In this paper, ensemble methods based on Choquet and Sugeno fuzzy integrals are proposed to combine the scores of three pre-trained deep learning models: ResNet50, DenseNet169, and InceptionV3. We tested the proposed methods on a public dataset and compared the results with already-used popular ensemble methods.