Abstract: A method and system for detecting a tunnel block falling disease based on visual and mechanical perception includes: capturing tunnel inner wall images, and preliminarily identifying a block falling disease using a trained first neural network model; constructing a circle based on a center which is the center position of the suspected disease determined in the preliminary identification process, dividing the circle equally through a plurality of diameters, pressing against the center of the suspected block falling disease in a pose perpendicular to the inner wall of the tunnel, moving in the diameter directions within the range of the suspected block falling disease, recording the displacement, and acquiring force and torque at the suspected block falling disease position during the movement; and determining the suspected tunnel block falling disease based on the acquired displacement, force and torque, using a second neural network model, and determining the tunnel block falling disease.

Abstract: A method and system for detecting a tunnel block falling disease based on visual and mechanical perception includes: capturing tunnel inner wall images, and preliminarily identifying a block falling disease using a trained first neural network model; constructing a circle based on a center which is the center position of the suspected disease determined in the preliminary identification process, dividing the circle equally through a plurality of diameters, pressing against the center of the suspected block falling disease in a pose perpendicular to the inner wall of the tunnel, moving in the diameter directions within the range of the suspected block falling disease, recording the displacement, and acquiring force and torque at the suspected block falling disease position during the movement; and determining the suspected tunnel block falling disease based on the acquired displacement, force and torque, using a second neural network model, and determining the tunnel block falling disease.