Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 2; p. 240
• Main Authors: Yu, Yang, Rashidi, Maria, Samali, Bijan, Yousefi, Amir M., Wang, Weiqiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Accuracy; Algorithms; Automation; Bearing capacity; Bridges; Classification; Classifiers; Concrete; Concrete bridges; concrete crack diagnosis; Concrete structures; Cracks; data fusion; Decision making; degradation; design; detection; Diagnosis; Diagnostic systems; durability; Edge detection; Feature extraction; Filters; Fuzzy sets; image processing; Infrastructure; machine learning; Medical imaging; Methods; Neural networks; Optimization; Partial differential equations; Pattern recognition; principal component analysis; Principal components analysis; Reinforced concrete; Remote sensing; sounds; Stiffness; Support vector machines; swarms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13020240

Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%.