The invention discloses a
shield tunneling machine attitude deviation measurement method based on
laser-vision
deep learning heterogeneous fusion, and the method comprises the steps: employing double sensors of a
laser target and a vision camera, carrying out the deployment of the sensors, and collecting data, which comprises
laser data and vision data; preprocessing the data, wherein the preprocessing comprises
laser data preprocessing and visual data preprocessing; the
laser data preprocessing comprises
laser data coordinate conversion,
laser target attitude deviation calculation and
noise filtering; the visual data preprocessing comprises image preprocessing, mark point detection, camera calibration and world coordinate calculation, and visual measurement attitude deviation calculation; a
deep learning heterogeneous fusion model is constructed, the
deep learning heterogeneous fusion model comprises a laser absolute feature
branch, a visual spatial-temporal feature
branch, an attention fusion layer and an output layer, and the model is trained; and inputting the preprocessed real-
time data into the trained deep learning heterogeneous fusion model, and carrying out dynamic calibration on an output result.