基于爬壁机器人的桥梁裂缝图像检测与分类方法

A bridge crack image detection and classification method based on a climbing robot

针对传统的桥梁裂缝检测方法成本高、工作环境危险的现状,提出一种基于爬壁机器人的桥梁裂缝图像检测与分类方法,即利用安装在爬壁机器人上的微型摄像镜头获取桥梁的壁面裂纹,通过图像处理和分析方法识别并对裂缝分类.首先对获取的图片去除运动模糊;然后运用小波变换对图像中的裂缝目标进行增强,再用二值图像面形态学分析提取裂缝目标,运用KD树对裂缝进行连接完成对裂缝图像的识别;最后运用支持向量机方法对裂缝实现分类,并与几何特征分类方法和基于BP神经网络的分类方法比较,结果表明,该方法对裂缝分类效果较好.

Traditional bridge crack detection methods are of high cost and high risk.A bridge crack detection and classification method was proposed based on a climbing robot using image analysis with a miniature camera mounted on the robot to collect images.First,the motion blur of acquired images was removed by Wiener filtering method.Second,wavelet transform was used to enhance the fractures of the crack in the image.Third,to complete crack image recognition,the surface morphology analysis is applied to extract crack fragments and then KD-tree was used to connect them.Finally,support vector machine method was used to classify crack images based on a series of basic visual characteristics and geometric features. Comparison of geometrical characteristic classification method and BP neural network classification method,results show that our method is better.