Rotor blades are very prone to deformation due to the harsh working environment. In order to monitor the edge state of rotor blades, this paper proposes a deep learning algorithm CACNet that can quickly segment the edge of rotor blades, a convolutional neural network for edge detection. Due to the high-energy X-ray image noise of the rotor blade, the dynamic blur is large, and the internal structure artifacts of the casing caused by high-energy X-ray transmission overlap in the same part of the image, resulting in extremely low image quality to be detected. For this low-quality image, the improved adaptive Canny operator is used to obtain the rough segmentation information of the image, which is used to assist the neural network to learn more accurate original information of the leaf edge. The model adopts a multi-scale structure, which can fuse the segmentation information at different scales, making the final result clearer and more accurate. In order to further improve the training quality, we also use a composite loss function, which can accurately guide the model to learn the correct information in the training image, so that the final model performs better on the real image. The experimental results show that the proposed algorithm has the ability to quickly and efficiently detect the edge of the rotor blade.