Total views : 141
Road Surface Crack Detection using Wavelets Features Extraction Technique
Evaluation of road pavement is an important task to maintain its quality. In a traditional way, officer checks the road surface by surveying along the road. This traditional method is less efficient because it requires extensive costs, takes a long time, exposes to safety issues, and less accurate due to human subjective factor and fatigue. The objective of this research is to develop a features extraction method based on wavelets to detect crack and non-crack road surface. The method involves road surface acquisition, pre-processing, features extraction using wavelets and classification task using linear discriminate analysis. The developed method was implemented on 56 images and produced 92.8% of accuracy detection of crack and non-crack.
crack detection, feature extraction, image processing, discrete wavelet transform.
- Rababaah H. Asphalt pavement crack classification: A comparative study of three AI approaches: Multilayer Perceptron, genetic Algorithms and Self-Organizing Maps [Thesis]. India: Department of Computer and Information Sciences; 2005.
- Adu-Gyamfi YO, Attoh-Okine N. Multi-resolution information mining and a computer vision approach to pavement condition distress analysis. CAIT-UTC-009; 2014.
- Federal Highway Administration. Pavement Distress Identification Manual. America: National Park Service; 2006-2009.
- Ling T-C, Mustaffar CPM. Automated Pavement Imaging Program (APIP) for pavement cracks cassification and quantification – A photogrammetric approach. Remote Sensing and Spatial Information Sciences. 2008; 37:B4.
- Subirats P, Dumoulin J, Legeay V, Barba D. Automation of pavement surface crack detection using the continuous wavelet transform. ICIP; 2006. p. 3040.
- Marques AGCS. Automatic road pavement crack detection using SVM. Lisbon: Electrical and Computer Engineering.2012.
- Gonzalez RC, Woods RE. Digital Image Processing. New Jersey: Prentice Hall; 2002.
- Darma P. Citra Digital dan Ekstraksi Fitur. Yogyakarta: Graha Ilmu; 2010.
- Riyadi S, Asnor Juraiza I, Mohd Marzuki M, Aini H.Wavelet-based feature extraction technique for fruit shape classification. Proceeding of the 5th International Symposium on Mechatronics and its Applications (ISMA08); Amman, Jordan. 2008.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.