Abstract: The present invention proposes a method for identifying the spatial-temporal distribution of the vehicle loads on a bridge based on the DenseNet. The method includes five steps: firstly, mounting a plurality of cameras in different positions of a bridge, acquiring images of the bridge from different directions, and outputting video images with time tags; secondly, acquiring multichannel characteristics of vehicles on the bridge by using DenseNet, including color characteristics, shape characteristics and position characteristics; thirdly, analyzing the data and characteristics of the vehicles from different cameras at a same moment to obtain vehicle distribution on the bridge at any time; fourthly, continuously monitoring the vehicle distribution in a time period to obtain a vehicle load situation on any section of the bridge; and finally, integrating the time and space distribution of the vehicles to obtain spatial-temporal distribution of the bridge.

Abstract: A structural vibration monitoring method based on computer vision and motion compensation provided in the present disclosure adopts a dual-camera system for self-motion compensation. The dual-camera system consists of a primary camera and a secondary camera rigidly connected to each other. The primary camera directly measures a structure displacement. This method inevitably includes an error generated due to motion of the primary camera. Meanwhile, the secondary camera measures displacements of translation and rotation, so as to estimate a measurement error caused by the motion of the primary camera. Then, with the displacement directly measured by the main camera minus the measurement error, a corrected structure displacement is obtained, thereby truthfully and accurately monitoring vibrations of a bridge structure.

Abstract: A method for modal analysis of bridge structures based on surveillance videos is provided. A plurality of continuous cameras are utilized for realizing high-precision surveillance for the structure of the bridge. Firstly, a plurality of regions are selected in the viewing angle of each of the cameras, and a conversion relation between images of the regions and actual displacements is calculated; next, displacements on the images shot by the cameras are converted into actual displacements of targets, so as to obtain displacements of the targets relative to the cameras; displacements of the adjacent cameras are sequentially passed according to a difference value from the ground of an abutment, so as to obtain displacements of all camera bodies; and finally, the displacements of all the targets relative to the ground of the abutment are calculated, and the structural mode of the bridge is calculated.

Abstract: A method for modal analysis of bridge structures based on surveillance videos is provided. A plurality of continuous cameras are utilized for realizing high-precision surveillance for the structure of the bridge. Firstly, a plurality of regions are selected in the viewing angle of each of the cameras, and a conversion relation between images of the regions and actual displacements is calculated; next, displacements on the images shot by the cameras are converted into actual displacements of targets, so as to obtain displacements of the targets relative to the cameras; displacements of the adjacent cameras are sequentially passed according to a difference value from the ground of an abutment, so as to obtain displacements of all camera bodies; and finally, the displacements of all the targets relative to the ground of the abutment are calculated, and the structural mode of the bridge is calculated.

Abstract: A structural vibration monitoring method based on computer vision and motion compensation provided in the present disclosure adopts a dual-camera system for self-motion compensation. The dual-camera system consists of a primary camera and a secondary camera rigidly connected to each other. The primary camera directly measures a structure displacement. This method inevitably includes an error generated due to motion of the primary camera. Meanwhile, the secondary camera measures displacements of translation and rotation, so as to estimate a measurement error caused by the motion of the primary camera. Then, with the displacement directly measured by the main camera minus the measurement error, a corrected structure displacement is obtained, thereby truthfully and accurately monitoring vibrations of a bridge structure.

Abstract: The present invention proposes a method for identifying the spatial-temporal distribution of the vehicle loads on a bridge based on the DenseNet. The method includes five steps: firstly, mounting a plurality of cameras in different positions of a bridge, acquiring images of the bridge from different directions, and outputting video images with time tags; secondly, acquiring multichannel characteristics of vehicles on the bridge by using DenseNet, including color characteristics, shape characteristics and position characteristics; thirdly, analyzing the data and characteristics of the vehicles from different cameras at a same moment to obtain vehicle distribution on the bridge at any time; fourthly, continuously monitoring the vehicle distribution in a time period to obtain a vehicle load situation on any section of the bridge; and finally, integrating the time and space distribution of the vehicles to obtain spatial-temporal distribution of the bridge.