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Automatic defects detection based on adaptive wavelet packets for leather manufacture
Author(s): He Fuqiang, Wang Wen, Chen Zichen
Source: International Technology and Innovation Conference 2006 (ITIC 2006), 2006, p. 2024 - 2027
Type: ConferencePaper
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The visual inspection system for leather surfaces was developed to quality control and raw material cut, an important component of automatic CAD/CAM cutting systems. The industrial detection of leather defects is difficult because of the large dimensions of the leather hides (3m×2.5 m), and the small dimensions of the defects (200μm×200μm). An efficient approach, using wavelet packets, is presented for the detection of defects embedded in leather surface images. Every inspection leather image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of leather images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural network classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual leather inspection.

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