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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