Aiming at the problem of the low recognition rate of the deep learning defect detection algorithm under the condition of small samples, a data enhancement method based on two-channel generative adversarial network is proposed. The generative adversance network is composed of two channels, such as global discriminator and local discriminator. The local discriminator can increase the confidence loss of the defect type and realize the enhancement of local information. The proposed method is used to conduct experiments on the lens defect image dataset. Experimental results show that the nearest neighbor index, maximum mean difference, and Wasserstein distance of the proposed method are 0.52, 0.15 and 2.81, respectively. For the defect type images of pitting, scratches, bubbles and foreign bodies, the generated image quality is better than that of conditional generated adversarial network, Wasserstein distance generated adversarial network and Markov discriminator. The lens image generated by the dual-channel generation confrontation network has diverse global information and high-quality detailed features, which can effectively enhance the lens defect data set.