Aiming at the complex phenomena such as variable background, bending and overlapping arrangement of hydraulic pipeline and the low accuracy of pipeline segmentation by existing image segmentation methods, a hydraulic pipeline segmentation method based on U-net network, combined with Mobilenetv3 network, squeeze-and-excitation networks module and self-calibration convolutional module is proposed. The method selected Mobilenetv3-large model as the backbone network, and the feature maps are processed with Lraspp network. In the decoding process, the squeeze-and-excitation networks module and self-correction module are integrated to improve the feature extraction ability. Finally, the combination of Dice function and BCE function is used as the loss function of the network, which effectively improves the convergence ability of the network. Experimental results show that the mean values of the proposed method in the intersection over union and pixel accuracy are 90. 8% and 95. 2%, respectively. The model size is 16. 9 M, and the reasoning time for each image is 20 ms, which can be applied to the scene requiring real-time deployment. It provides a basis for the accurate identification of hydraulic pipeline leakage.