Classification of defects in steel strip surface based on multiclass support vector machine
Multimedia Tools and Applications
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The particle swarm optimization was applied in BP neural network training. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in realities. Meanwhile, PSO-BP neural network is applied into classification of fabric defect. The method of orthogonal wavelet transform was used to decompose monolayer from fabric image. And the sub-images of horizontal and vertical direction are extracted to represent respectively the textures of fabric in warp and weft. Compared classification of PSO-BP neural network to classification of BP neural network, it is shown that PSO-BP neural network achieves favorable results.