改进Hough变换的农作物病斑目标检测方法

为了能够快速、准确地对农作物病斑进行图像检测,该文根据病斑的形态特点,提出一种基于边缘检测与改进Hough变换的病斑目标检测方法。该研究根据不同种类的病害图像,采用R、G、B或者之间的差值分量确定病斑的特征图像,采用边缘提取、修复、过滤等方法获取病斑轮廓。对Hough变换的应用策略进行改进,采用边缘线编码,每个病斑根据自身形态确定变换的参数,并采用对应的圆形对病斑边界进行拟合,从而对病斑进行检测,同时对病斑边界进行有效识别。以90幅不同种类农作物病害图像为研究对象,对病斑进行类圆目标检测,检测圆拟合精度为87.01%,圆心定位误差为4.44%。结果表明,该方法能够快速、准确地对类圆病斑进行检测,同时对病斑边界有较好的识别效果。

A method of target detection for crop disease spots by improved Hough transform

Abstract: Image detection of crops' disease spots can be helpful for real-time monitoring and diagnosis of diseases and pests. Plant Disease Images were diverse and complex. Many methods have been studied to detect and recognize disease spots. In order to realize real-time and accurate detection, the detection algorithm of disease spots must be both steady and simplified. This paper proposed a rapid detection method of disease spots based on edge detection and improved Hough transform. Edge detection needed a gray image. Disease images were calculated into feature images by R, G, B, or difference components, according to disease spots in the images. The edges were first detected by a Canny operator. A mended template was designed to repair the broken edge into the close edge. The close edge was picked by morphological operation. The close edges contained disease spots, also other background edges. Farther filtration was needed to obtain the close edge of disease spots. Edge detection and repair was the base for the Hough transform. As the disease spots grew and developed approximately circular, round Hough transform was improved to detect the edge of the disease spot. Direct application of Hough transform to the disease spots edges would result in great calculation and inaccuracy fitting, as there might be a large gap between the radiuses of the disease spots. The edges of disease spots were coded and divided into quasi-circular disease spots and irregular disease spots, according to the ratio between the long radius and the short radius of each spot. The quasi-circular disease spots were fitted by a single circle, the irregular disease spots were fitted by multiple circles. The transform parameters of each edge were determined according to the disease spots edge. Hough transform were carried out on each edge in independent space. So that error accumulation could be avoided. The largest intersected Hough circle would be kept. The edge of the disease spot was fitted by the circular and the center of the disease spot so it could be located. 90 crop diseases images were used to detect disease spots. These images which included leaf spots and fruit spots were randomly pictured in the field. The detection algorithm was programmed to carry out the detection. The results showed that the detected circles covered most of the disease spots; centers of the disease spots were also well located. Statistics results showed that the circle fitting accuracy was 87.01%, the central error was 4.44%. The running times of the Hough transform for each image were less than 5 s. The results demonstrated that this detection algorithm could detect disease spots both quickly and accurately. Meanwhile, the edges of the disease spots could be effectively identified and better recognized.