Visual quality recognition of nonwovens based on wavelet transform and LVQ neural network

An approach to identify visual quality of nonwoven products by combining wavelet transform and learning vector quantization (LVQ) neural network is proposed in this paper. 625 nonwoven images of 5 different visual quality grades, each including 125 images, are decomposed at four different levels using five wavelet bases of the Daubechies family. The energy values L² extracted from the high frequency subbands are used as the input features of the LVQ neural network. In our research, comparative experiments are employed to evaluate the performance of the proposed method, which takes into account three effect factors, including the wavelet base (the length of filter), the decomposition level and the size of training set. Experimental results show that this approach can lead to high degree of success rate in nonwoven visual quality recognition.