Abstract
This paper presents a new contribution for the problem of automatic visual inspection. New methods for determining threshold values for fabric defect detection using feedforward neural networks are proposed. Neural networks are one of the fastest most flexible classification systems in use. Their implementation in defect detection, where a clear classification is needed, requires thresholding the output. Two methods are proposed for threshold selection, statistical analysis of the NN output and confusion matrix based optimization. Experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have confirmed their usefulness.
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Castilho, H.P., Pinto, J.R.C., Serafim, A.L. (2006). NN Automated Defect Detection Based on Optimized Thresholding. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_71
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DOI: https://doi.org/10.1007/11867661_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
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