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Abstract: The detection of the surface defects on magnetic sheet has played an important role in the production efficiency and the cost of production in the production line of the magnetic sheet factory. A variety of machine vision methods has been applied, they are taken to extract features of artificial defects, but because the disk surface has low contrast, wear texture interference and small changes in the brightness and defects of the difficulties, they lead to less accuracy and versatility; in addition ,it’s easy to obtain the huge data in the actual production volume ,but manual annotation has the high cost; this paper propose a deep active learning method of disk surface defect solve the above two problems; firstly, the template matching algorithm with edge detection will segment the disk foreground and background; secondly, the samples are trained using Inception-Resnet-v2 deep neural network, completing the identification of defect image; finally, in the deep learning process, proposes an active learning method to overcome the large data set but the annotation cost high. The experimental results show that the detection recognition rate of the proposed method reaches 96.7% and can save up to 25% of the cost of human annotation.