A method for segmentation of underground drainage pipeline defects based on full convolutional neural network includes steps of: collecting a data set of the underground drainage pipeline defects; processing the data set of the underground drainage pipeline defects; optimizing with a semantic segmentation algorithm; adjusting model hyperparameters; training a model; verifying the model; and testing the model. The method adopts a deep learning algorithm, optimizes the FCN full convolutional neural network, develops a semantic segmentation method suitable for complex and similar defect characteristics of underground drainage pipelines, and adopts real underground drainage pipeline defect detection big data, thereby realizing pixel-level segmentation of the underground drainage pipeline defects and providing better robustness and generality. The detection accuracy and efficiency of the underground drainage pipeline defects are effectively improved.