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Recognition of Concrete Surface Cracks Using Enhanced Max-Min Neural Networks

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2007, 12(2), pp.77-82
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Park, Hyun-Jung 1 Kwang Baek Kim ORD ID 1

1신라대학교

Accredited

ABSTRACT

In this paper, we proposed the image processing techniques for extracting the cracks in a concrete surface crack image and the enhanced Max-Min neural network for recognizing the directions of the extracted cracks. The image processing techniques used are the closing operation of morphological techniques, the Sobel masking for extracting for edges of the cracks, and the iterated binarization for acquiring the binarized image from the crack image. The cracks are extracted from the concrete surface image after applying two times of noise reduction to the binarized image. We proposed the method for automatically recognizing the directions of the cracks with the enhanced Max-Min neural network. Also, we propose an enhanced Max-Min neural network by auto-tuning of learning rate using delta-bar-delta algorithm. The experiments using real concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

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