Review and Analysis of Crack Detection and Classification Techniques based on Crack Types

In real time scenario, cracks are very common in building, bridge, road, pavement, railway track, automobile, tunnel and aircraft. The presence of crack diminishes the value of the civil infrastructure and hence it is necessary to estimate the severity of crack. Crack detection and classification techniques with quantitative analysis play a major role in finding the severity of crack. The various quantitative metrics are length, width and area. Due to the rapid development in technology, number of images acquired for analysis is growing enormously. Therefore, automatic crack detection and classification techniques for civil infrastructure are essential. This paper focuses on three objectives: (i) Analysis of various crack detection and classification techniques based on crack types (ii) Implementation of Otsu’s based thresholding method for crack detection (iii) Design of proposed system.

[1]  Paulo Lobato Correia,et al.  Identifying and retrieving distress images from road pavement surveys , 2008, 2008 15th IEEE International Conference on Image Processing.

[2]  Chao Zhou,et al.  Surface Distresses Detection of Pavement Based on Digital Image Processing , 2010, CCTA.

[3]  Habibullah Khan.  Image enhancement using Wavelet transforms and SVD , 2012 .

[4]  Hoang-Nam Nguyen,et al.  A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform , 2012, 2012 International Symposium on Intelligent Signal Processing and Communications Systems.

[5]  E. Salari,et al.  An image-based pavement distress detection and classification , 2012, 2012 IEEE International Conference on Electro/Information Technology.

[6]  Khurram Kamal,et al.  Pavement crack detection using the Gabor filter , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[7]  Paulo Lobato Correia,et al.  Automatic Road Crack Detection and Characterization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[8]  Yun Liu,et al.  Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring , 2014, Sensors.

[9]  Chung-Ming Yang,et al.  Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis , 2015, Adv. Eng. Softw..

[10]  Carlos Eduardo Pereira,et al.  Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs , 2015 .

[11]  Zhangcan Huang,et al.  Shape-based Micro Crack Detection in Plastic through Image Processing , 2016 .

[12]  Kristin J. Dana,et al.  Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.

[13]  Meng Qiao,et al.  Research on a least squares thresholding algorithm for pavement crack detection , 2016, 2016 Sixth International Conference on Information Science and Technology (ICIST).

[14]  Sidney Nascimento Givigi,et al.  Automatic Crack Detection and Measurement Based on Image Analysis , 2016, IEEE Transactions on Instrumentation and Measurement.

[15]  Jérôme Idier,et al.  Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection , 2016, IEEE transactions on intelligent transportation systems (Print).

[16]  Li Feng,et al.  A bridge crack image detection and classification method based On climbing robot , 2016, 2016 35th Chinese Control Conference (CCC).

[17]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[18]  Lu Sun,et al.  Sealed-Crack Detection Algorithm Using Heuristic Thresholding Approach , 2016, J. Comput. Civ. Eng..

[19]  Jaime Gómez García-Bermejo,et al.  Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation , 2017, Sensors.

[20]  Anisha M. Lal,et al.  Crack Detection using Image Processing for Automobiles and Aircrafts , 2017 .

[21]  Xinnan Fan,et al.  A novel underwater dam crack detection and classification approach based on sonar images , 2017, PloS one.

[22]  Shafeeq Ahmad,et al.  Crack Detection in Railway Track Using Image Processing , 2017 .

[23]  S. Tighe,et al.  Three-Dimensional Pavement Crack Detection Algorithm Based on Two-Dimensional Empirical Mode Decomposition , 2017 .

[24]  Kamel Besbes,et al.  Automatic crack detection from pavement images using fuzzy thresholding , 2017, 2017 International Conference on Control, Automation and Diagnosis (ICCAD).

[25]  Sumathi Poobal,et al.  Crack detection using image processing: A critical review and analysis , 2017, Alexandria Engineering Journal.