Aiming at the problem that the small number of samples in the dataset will affect the effect of deep learning detection, a strip defect classification method based on improved generative adversarial networks and MobileNetV3 is proposed in this paper. First, a generative adversarial network is introduced, and the generator and discriminator are improved to solve the problem of category confusion and realize the expansion of the strip defect data set. Then, the lightweight image classification network MobileNetV3 is improved. Finally, it is trained on the expanded data set to realize the classification of strip defects. Experimental results show that the improved generative adversarial network can generate more real strip steel defect images and solve the problem of insufficient samples in deep learning. And the parameter amount of the improved MobileNetV3 is about 1/14 of that before improvement, and the accuracy is 94.67%, which is 2.62 percentage points higher than that before improvement. It can be used for accurate and real-time classification of strip steel defects in industrial field.