... implemented in Matlab for automatic detection and classification of defects based on digital images analysis combined with machine learning algorithms such as the random forest algorithm and boosting. Segmentation is implemented using graph-cuts method and Markov random fields. The efficiency of proposed approach is demonstrated on the real data set. © 2018 - IOS Press and the authors. All rights reserved. входит
Теги: a random forest algorithm , boosting algorithm , graph-cuts method , markov random fields , pavement condition , road defects , adaptive boosting , artificial intelligence , decision trees , defects , feature extraction , graphic methods , image segmentation , learnin... system includes the following steps. First step is to detect defect position (ROI) then the defect is described by its features. Finally, each defect is classified these different defect features. In our approach the following algorithms have been using: Markov Random Fields and Graph cuts method for image segmentation, Random Forests algorithm for data classification. нет
Теги: feature extraction , defect pavement , defects detection , markov random fields , graph cut , random forests , computer vision
