Carbon fiber composite core wires can greatly increase transmission capacity of transmission lines. However, many breaks are caused due to the bending resistance and other reasons, which seriously endangers the safety of line operation. In order to realize on-line defect detection for long-distance transmission lines, this paper proposes an automatic defect detection scheme for carbon fiber composite core conductors. Firstly, the X-ray image of carbon fiber composite core conductors is standardized. Then，data consistency is improved to provide conditions for automatic analysis of conductors after bending compensation and brightness normalization. Finally, the deep convolution neural network technology is used for defect detection. Experiments on aluminum conductor composite core (ACCC) show that the scheme can quickly and automatically identify the defects of carbon fiber composite core conductors.