Crack detection in wooden pallets using the wavelet transform of the histogram of connected elements

Authors:
M. A. Patricio;D. Maravall;L. Usero;J. Rejón
Affiliations:
Dpto. Ciencias de la Computación, Universidad de Alcalá, Spain;Dpto. de Inteligencia Artificial, Universidad Politécnica de Madrid, Spain;Dpto. Ciencias de la Computación, Universidad de Alcalá, Spain;Dpto. Ciencias de la Computación, Universidad de Alcalá, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

The paper presents the application of the wavelet transform of the frequency histogram of connected elements to the detection of very thin cracks in used pallets. First, the paper presents this novel concept and introduces the parameters that define a connected element, showing that the conventional grayscale intensity histogram of a digital image is a particular case of the histogram of connected elements. Then, the discriminant capability of the wavelet transform of this generalized histogram is analyzed. In particular, the information conveyed by the histogram of connected elements is exploited to detect very thin cracks in used pallets. An artificial neural network classifier to discriminate sound wood from defective wood with very thin cracks has been designed. The exhaustive experimental test carried out with numerous boards of used pallets has validated the proposed method, in particular its remarkably low ratio of false alarms.