The invention provides a belt tearing
rapid detection and early warning device and method based on
artificial intelligence, and relates to the technical field of equipment detection. According to the device provided by the invention, a monitoring unit is arranged at a blanking port, and a high-speed CCD (
Charge Coupled Device) camera mounted at a 45-degree
elevation angle works cooperatively with a
laser transmitter to capture a line
laser image at the bottom of a belt in real time; the core technology of the
rapid detection and early warning method comprises the following steps: 1) an improved Sobel enhancement operator is used for inhibiting dust and water
stain interference while keeping a real edge; 2) performing multi-scale LBP feature analysis, and accurately extracting crack textures in combination with dynamic threshold segmentation; the method comprises the following steps of (1) building a deformation index DI model to realize grading early warning, (2) building a deformation index DI model to realize grading early warning, (3) capturing space-time continuity through a 3D
convolution kernel and realizing longitudinal crack detection in cooperation with the curvature quantification model, (3) building a
distortion index DI model to realize grading early warning, (4)
iron ore field
verification shows that the transverse crack
detection rate and the longitudinal crack early warning accuracy are both higher than those in the prior art, the
false alarm rate in a dust environment is as high as 7%, and material leakage and safety accidents are effectively avoided.