•A dataset generation method in large depth of field is proposed.•A 3D displacement measuring device is applied to measure the expansion of boiler.•The MOGA can improve the efficiency and precision of ANN simultaneously.•The PSF can reduce pixel coordinate errors by half compared to one that does not include it. In this paper, an acronym for stereo vision calibration-point spread function (SVC-PSF), is designed to accurately represent image degradation in large-depth-of-field (LDOF). The error of the dataset generated by the SVC-PSF is 50 % lower than that of Stereo Vision Calibration (SVC) method in LDOF. A three-dimensional point coordinate reconstruction artificial neural network (ANN) is improved through a multi-objective genetic algorithm (MOGA) to satisfy both computational accuracy and real-time performance. The MOGA improved ANN can reconstruct 1250 points within 0.069 s on an embedded platform. Finally, SVC-PSF is used to generate a dataset, and the MOGA-ANN improved model is trained based on this dataset. An embedded device, stereo displacement measurement device (SDMD) is designed to run SVC-PSF-MOGA-ANN algorithm, and this device is applied in thermal power plant boiler expansion monitor. The real-time monitor results shows that when the boiler power increases, more working medium is needed to produce steam, so the boiler's own weight increases, and the boiler expands downward under the action of gravity. At low power, the required working medium is reduced, and the corresponding expansion amount is also reduced.