The aluminum plate surface defects recognition method of BP neural network is studied based on target detection .In order to detect the defects, the target image is binaried by adaptive threshold method. After binarizing the target image, three kinds of image feature, including geometric feature, grayscale feature and shape feature, are extracted from the target image and its corresponding binary image. The defects classification model based on back-propagation neural network utilizes three layers neural network structure model and the hyperbolic tangent function of S function as the activation function, the number of neurons in hidden layer is confirmed by experiments. The experimental results show that the classification accuracy of BP neural network classification model as high as 94%, this can meet our requirements.
Multi-Index Classification Model for Loess
Deposits Based on Rough Set and BP
Neural Network
