自适应阈值Prewitt的石榴病斑检测算法  

Algorithm for detecting pomegranate disease spots based on Prewitt operator with adaptive threshold

作　　者：巨志勇[1] 薛永杰 张文馨 翟春宇 Ju Zhiyong;Xue Yongjie;Zhang Wenxin;Zhai Chunyu(School of Optical-Electrical and Computer Engineering,University of Shanghai for Sciences and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《农业工程学报》2020年第8期135-142,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(81101116)。

摘　　要：针对传统识别方法对石榴外表病斑及石榴轮廓检测精准度不高、抗噪声能力不强以及存在伪边缘等问题。该文提出一种基于自适应阈值Prewitt算子的石榴病斑检测算法。采用双边滤波减少噪声干扰;通过高频强调滤波提高图像高频分量,增强局部细节;根据高斯噪声概率分布设置算子卷积掩膜元素权重,利用对称性将方向梯度两两组合,并计算其L2范数作为该像素点的梯度。对人工拍摄的607张石榴图像进行图像增强和边缘检测试验,加入椒盐噪声和高斯噪声进行抗噪性能测试。试验结果表明,该文算法对石榴病斑的识别正确率为98.24%,获得图像的峰值信噪比为43.72 dB,单张图像识别耗时为0.174 s。该研究具有较好的病害样本与非病害样本区分能力,可为田间环境下石榴病害预防提供参考。Pomegranates are one of the economic fruits in China. The timely detection of pomegranate diseases and the corresponding preventive measures are important to increase crop yields and reduce economic losses. To tackle the issues that traditional edge detection operators usually resulted in low accuracy of the detection of pomegranate diseases and its contour, low anti-noise capability, and created false edges, this study presented an improved Prewitt operator with an adaptive threshold. Firstly, in the image pre-processing stage, the pending image was enhanced by high-frequency emphasize filter, and the high-frequency component, which represented to the details of the pomegranate sample in the image, was increased by the filter. On the contrary, the low-frequency component, which represented to the background of the pomegranate sample, was attenuated to facilitate the following image processing. Since the most original images contained the Gaussian noise, bilateral filtering was used to process the noise present in the image. The weighted average of the brightness values of adjacent pixels was used to represent the intensity of a pixel. The weighted average method was used based on Gaussian distribution. The weight calculation took into account both the Euclidean distance between the pixels and the radiation difference in the pixel range domain to better maintain edge feature information. Secondly, a fifth-order convolutional mask was proposed. The weights of the elements in the mask were set according to the properties of the Gaussian noise probability distribution to reduce the effect of noise on the algorithm. The weight of the central element of the convolutional mask was increased so that the edge information of the image had higher contrast. In terms of gradient calculation, eight direction templates were used to perform the convolutional operation of the image, then the gradient values of each direction were calculated. After that, the corresponding gradient values were obtained, and the gradient values of

关 键 词：水果 算法 病害 边缘检测 图像增强 最小误差法 双边滤波 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]