The Application Of Associative System Of LabVIEW And ARM Embedded In Rock Crack Detection

In the field of geological structures,the stability of rock masses will be reduced due to the excavation of underground mines,excessive use of road surfaces and bridges,and the extrusion of the surrounding structures of tunnels.In order to effectively limit the progressive damage of fractured rock masses,the detection of cracks in rock masses is not only of theoretical significance but also has practical value and social benefits.On the one hand,the deformation and failure process of rock mass is essentially the process of initiation,expansion,interaction,and penetration of cracks under conditions of engineering disturbance.On the other hand,complex fracture forms in the rock mass change the stress state of the rock mass,further affecting the failure mode and instability process of the rock mass.Therefore,the detection and research of cracks in rock masses are of great practical significance.In general geological structures,common cracks in rock masses include mountain cracks,bridge cracks,tunnel cracks,and highway cracks.However,according to the current situation in China,the specific implementation process of this type of crack detection is very difficult,because in general the cracks in the rock are in a harsh environment,coupled with the current domestic detection technology is relatively backward.As a result,the exploration process for this type of geological phenomenon is inefficient and it is difficult to adapt to actual site requirements.signal.In order to facilitate the rapid detection requirements,a new method idea is proposed and perfected in practical application and practice.This paper adopts a self-designed and assembled UAV to collect and inspect crack images.The UAV is based on the ARM V7 architecture Cortex-M4 core STM32 architecture,using LabVIEW software to burn the control program to the main control board,according to the needs of the motor,ESC,air paddle,receiver,image transmission module,digital transmission Modules,ultrasonic sensors,etc.exercise.In the process of intelligent identification of cracks,I used the OpenMV embedded machine vision module,mounted the module on a drone,and gave it a slightly better zoom lens,allowing the drone to take pictures of rock cracks.During the process,I can quickly and accurately identify the area I want to use for research.According to the picture transmitted by the picture transmission module,and then send the shooting instruction via the remote,the recognized image is saved to the OpenMV.TF card,and the actual need to analyze the screen shot and stored by the camera.How to deal with the collected images,this paper uses the image processing software Halcon,which is more advanced in the field of machine vision.It uses its syntactic conciseness,rich library functions,and simple requirements for equipment,and it is processed in Halcon's integrated development environment,HDevelop.After processing the obtained image,it finally proves that the system can accurately detect cracks in the rock mass,and the image processing result is also satisfactory,with practicality and feasibility.