Title :
Pavement distress classification using neural networks
Author :
Chou, JaChing ; O´Neill, W.A. ; Cheng, H.D.
Author_Institution :
Dept. of Civil & Environ. Eng., Utah State Univ., Logan, UT, USA
Abstract :
A novel approach of applying moment invariants and neural networks to analyze pavement images is presented in this paper. By calculating moment invariants from different types of distress, features are obtained. Then a backpropagation neural network is used to classify these features. This approach is illustrated using randomly selected sample of video images of real cracks. Based on these samples, the feasibility of using moment invariants and neural networks to classify different types of crack is proven
Keywords :
backpropagation; civil engineering computing; engineering; government data processing; image classification; neural nets; video signal processing; backpropagation neural network; moment invariants; neural networks; pavement distress classification; pavement images; video images; Entropy; Equations; Filters; Image enhancement; Image segmentation; Interpolation; Neural networks; Shape; Smoothing methods; Transforms;
Conference_Titel :
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2129-4
DOI :
10.1109/ICSMC.1994.399871
