In this paper, an automated vision system is presented to detect and classi
fy surface defects on leather fabric. Visual defects in a gray-level image
are located through thresholding and morphological processing, and their ge
ometric information is immediately reported. Three input feature sets are p
roposed and tested to find the best set to characterize five types of defec
ts: lines, holes, stains, wears, and knots. Two multilayered perceptron mod
els with one and two hidden layers are tested for the classification of def
ects. If multiple line defects are identified on a given image as a result
of classification, a line combination test is conducted to check if they ar
e parts of larger line defects. Experimental results on 140 defect samples
show that two-layered perceptrons are better than three-layered perceptrons
for this problem. The classification results of this neural network approa
ch are compared with those of a decision tree approach. The comparison show
s that the neural network classifier provides better classification accurac
y despite longer training times.