基于U-Net与河马优化的机器视觉驱动的大坝粗差识别方法OA

To address the limitations of traditional monitoring methods,such as susceptibility to noise,low error identification rates,and insufficient robustness that fail to meet high-precision safety monitoring requirements,a machine vision-based error identification method is proposed.By analyzing the architecture and technical logic of U-Net systems,this approach simulates human visual data perception mechanisms to transform one-dimensional time-series monitoring data into more discriminative high-level semantic features.To reduce the uncertainties caused by hyperparameter combinations and manual post-processing strategies in U-Net models,a Hippo optimization algorithm is incorporated for global optimization,thereby enhancing the model's robustness against hidden coarse errors.Practical engineering validation demonstrates significant improvements in error identification rates compared to conventional machine learning algorithms,particularly showing outstanding performance under multi-source noise interference and nonlinear data scenarios.This innovation provides a novel solution for error identification in dam safety monitoring systems.