Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network

Automatic recognition method for hot-rolled steel strip surface defects is extremely important to the steel surface inspection system. In order to improve the tolerance ability of local deformations for current feature extraction methods, a scattering operator is applied to extract features for defect recognition. Firstly, a scattering transform builds non-linear invariants representation by cascading wavelet transforms and modulus pooling operators, which average the amplitude of iterated wavelet coefficients. Then, an improved network named the scattering convolution network (SCN) is introduced to build largescale invariants. Finally, a surface defect database named the Northeastern University (NEU) surface defect database is constructed to evaluate the effectiveness of the feature extraction methods for defect recognition. Experimental results demonstrate that the SCN method presents the excellent performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes. Even in the less number of training, the SCN method can still achieve the moderate recognition accuracy. © 2014 Binary Information Press.