In order to realize quantitative imaging of pipe wall corrosion defects, an imaging algorithm based on physics embedded convolution neural network is proposed to reconstruct pipe wall thickness from ultrasonic guided wave signals. Firstly, the twodimensional acoustic wave model of ultrasonic guided wave propagation on the pipe wall is derived. The wave equation in frequency domain can be solved by matrix LU decomposition to realize the forward modeling from the pipe wall guided wave velocity diagram to the acoustic field signal. Secondly, the physics embedded convolution neural network is built, including three iterative layers, each of which is composed of forward model and residual inversion subnetwork. The pipeline simulation data set containing random corrosion defects is generated, and the network is built for training and inversion. The average Pearson correlation coefficients of the imaging results of the training set, verification set and test set are 94. 91%, 86. 47% and 87. 37% respectively, and the defect image consistency is high. The experimental system is built, and the guided wave signal is collected on the pipe with irregular step defects for inversion. The imaging results is remarkable, with a mean square error of 0. 005 7 for the thickness map. The algorithm combines the physical model with neural network to achieve high-precision imaging from guided wave signal to pipeline thickness map.