Improved Sacked Denoising Autoencoders-Based Defect Detection in Bar Surface

Traditional pattern recognition methods are widely used to detect defects in the industry, however most of existing methods are not universal for all kinds of defects on bars. This paper proposes a method which combines Rectified Linear Units (ReLU) and Batch Normalization(BN) in Sacked Denoising Autoencoders(SDA) for surface defect detection of bars. Gradient diffusion often occurs in traditional SDA, which leading to inefficient learning. In order to solve gradient diffusion, we replace the Sigmod activation function with ReLU function. The activation value calculated by ReLU is often oversparing which leading to loss of features. For solving the oversparing of ReLU, we add BN layer into SDA to normalize each batch. Finally we obtain network weights through unsupervised pre-training and supervised fine-tuning. We train two models which one for prediction and the other for reconstruction. Experiment results show that the proposed method can achieve an average accuracy rate of 99.1% on our data set. Compared with the traditional pattern recognition method, traditional SDA and Fisher criterion-based stacked denoising autoencoders(FCSDA), our method shows higher accuracy and TPR. Moreover, due to the addition of BN, the time complexity of our method is significantly lower than the SDA and FCSDA.