基于自适应阈值分割改进Canny算子的管道边缘检测方法研究

Research on pipeline edge detection based on improved Canny operator with adaptive threshold segmentation

针对工厂中管道破损位置无法通过机器视觉准确判断的问题,提出一种基于自适应阈值分割改进Canny算子的管道边缘检测方法。该方法从滤波方式、梯度方向以及阈值分割角度对采集图像进行处理,首先采用采样-自适应中值滤波+双边滤波代替传统Canny算子中的高斯滤波,减少图像边缘信息丢失并去除图像中的噪声,然后增加梯度幅值的计算来更好地检测不同方向的边缘信息,最后为避免人工选取阈值效果不佳的情况,采用最大类间方差(OTSU)阈值分割算法进行阈值的自适应选取。实验表明,该方法相比于传统Canny算子的图像信噪比提升28.22%,边缘点数提升39.97%,四连通道数提升11.52%,八连通道数提升5.92%,提取特征完整且连续性较好,实现了对管道图像中破损情况的有效检测。

To solve the problem that the location of broken pipes in a factory cannot be accurately determined by machine vision,a pipeline edge detection method based on improved Canny operator with adaptive threshold segmentation is proposed.The method processes the acquired images in terms of filtering method,gradient direction and threshold segmentation.Firstly,sampling-adaptive median filtering+bilateral filtering is used instead of Gaussian filtering in the traditional Canny operator to reduce the loss of image edge information and remove the noise in the image.Then,the gradient amplitude is calculated to detect the edge information in different directions.Finally,to avoid the ineffective manual selection of thresholds,the OTSU threshold segmentation algorithm is used for adaptive selection of thresholds.Experiments show that the method improves the image signal-to-noise ratio by 28.22%,the number of edge points by 39.97%,the number of four-connection channels by 11.52%and the number of eight-connection channels by 5.92%compared to the conventional Canny operator.The extracted features are complete and have good continuity,enabling effective detection of breakage in pipeline images.