An optimum feature extraction method based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification

Authors:
Fereidoon Moghadas Nejad;Hamzeh Zakeri
Affiliations:
Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran;Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 6
Cited 1

A Fast Discrete Approximation Algorithm for the Radon Transform

SIAM Journal on Computing
Pattern recognition by means of the Radon transform and the continuous wavelet transform

Signal Processing
Detecting line segments in an image: a new implementation for Hough transform

Pattern Recognition Letters
Wavelets for Computer Graphics: A Primer, Part 1

IEEE Computer Graphics and Applications
A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

Computers in Biology and Medicine
A fast Hough transform for segment detection

IEEE Transactions on Image Processing

Easy-to-explain feature synthesis approach for recommending entertainment video

Neurocomputing

Quantified Score

Hi-index	12.05

Visualization

Abstract

Quantification of pavement crack data is one of the most important criteria in determining optimum pavement maintenance strategies. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. This paper present an automatic diagnosis system for detecting and classification pavement crack distress based on Wavelet-Radon Transform (WR) and Dynamic Neural Network (DNN) threshold selection. The algorithm of the proposed system consists of a combination of feature extraction using WR and classification using the neural network technique. The proposed WR+DNN system performance is compared with static neural network (SNN). In test stage; proposed method was applied to the pavement images database to evaluate the system performance. The correct classification rate (CCR) of proposed system is over 99%. This research demonstrated that the WR+DNN method can be used efficiently for fast automatic pavement distress detection and classification. The details of the image processing technique and the characteristic of system are also described in this paper.