Deep neural network is one of the mainstream surface defect detection methods, a large number of samples are needed for model training, but the defect samples of the same type of ceramic tile are limited with the diversification of ceramic tile products. In this paper, a ceramic tile surface defect detection method based on improved domain countermeasure neural network (MDANN) is proposed. Referring to the traditional DANN structure, the network parameters are pretrained on the ImageNet to improve the training speed. Then, the bottleneck layer is added to the original network, and the maximum mean difference index is used to optimize the field distribution difference, which improves the ability of the original DANN network to screen the source domain and realizes the defect detection of small sample tiles. The experimental results show that the effective detection rate of MDANN for ceramic tile surface defects achieves 98.77%, which is 3.53% higher than the original DANN network. It can be quickly applied to the detection of different types of ceramic tiles with good generalization.