An inspection model for surface defects of copper strips based on vision bionics is proposed in this paper. The model firstly uses Gaussian pyramid and Gabor filter to extract the features of defective images

and combines the features into a saliency map. Then

it simulates the bottom-up attention mechanism

analyzing the content of fovea to acquire “what” information and obtaining a time-ordered sequence of visited regions after simulation of saccades

which constitutes the “where” stream. At last

it uses a discrete and observable Markov model to adjust the probabilities of a single Markov chain according to “what” and “where” stream

and maximizes the likelihood of the training data

and realizes correct classification. The experimental results demonstrate the feasibility and effectiveness of the proposed method in surface defect inspection

furthermore

the average accuracy rate can reach 94.40%.