基于机器视觉的装配式建筑混凝土结构损伤检测

Damage detection for prefabricated building concrete structures based on machine vision

随着装配式建筑的普及,现有混凝土结构损伤检测方法容易受到噪声环境影响的问题亟待解决,设计了一套基于机器视觉的装配式建筑混凝土结构损伤检测方法。该机器视觉采集装置由机器视觉工具(由图像采集卡、CCD摄像头构成)和爬壁机器人构成,将机器视觉工具搭载在爬壁机器人上完成图像采集。首先,使用加权平均法对采集的图像实施灰度化处理,使用阈值滤波法实施灰度图像的滤波处理;其次采用由快速区域卷积神经网络FastR-CNN与区域推荐网络RPN组成的Faster R-CNN模型实现混凝土结构损伤检测。试验结果表明,该方法在噪声环境下能够实现有污渍混凝土表面、阴影混凝土表面、粗糙混凝土表面的准确结构损伤检测,检测系统的鲁棒性较强。

With the popularization of prefabricated buildings,the existing damage detection methods for concrete structures are easily affected by noise environment,which is an urgent problem to be solved.Therefore,a machine vision-based damage detection method for prefabricated concrete structures was designed.The machine vision acquisition device consisted of machine vision tools including image acquisition cards and CCD cameras and a wall climbing robot.The machine vision tools were mounted on the wall climbing robot to achieve image acquisition.Firstly,the collected images were grayscale processed using the weighted average method,and the grayscale images were filtered using the threshold filtering method;Secondly,the Faster R-CNN model consisting of Fast Regional Convolutional Neural Network and Regional Recommendation Network(RPN)was used to achieve damage detection in concrete structures.The experimental results showed that this method can achieve accurate structural damage detection of stained concrete surfaces,shaded concrete surfaces,and rough concrete surfaces in noisy environments,and the detection results were robust.