The rebar represents an essential material in civil engineering. In the rolling process, roll wear, billet quality, and other factors will cause surface defects. If they cannot be detected in time, a large number of waste products will be produced, seriously affecting the economic benefits of enterprises. In this study, a detection method of rebar defects based on deep learning is proposed. Images of rebars are collected by industrial cameras in the production site, and their surface defects are classified and labeled to establish sample datasets that are further enhanced by the Deep Convolutional Generative Adversarial Network (DCGAN). Faster RCNN is adopted to construct the detection model of rebar defects, which can identify the surface defects in a small sample size with migration learning. In addition, the detection model is optimized through the evaluation of the setting of loss function, optimization methods, learning rates and sliding average. The experiment reveals that the method can effectively solve the problems of low efficiency and high false detection rates caused by manual detection, with good stability and practicability.