美国白蛾幼虫网幕图像识别算法

Image Recognition Algorithm of Hlyphantria cunea Larva Net

摘要:

根据美国白蛾幼虫网幕图像色彩分布特征，选择RGB颜色空间，分析网幕、叶片和树枝的各通道数据的差值，采用R—B色差模型并结合最大类间方差法和阈值算法，分割网幕图像。使用Freeman编码算法和区域标记计算出每一区域的面积，使用多个面积的平均值和标准方差确定面积双阈值，进行残余噪声去除。根据面积分别对大片白色区域和细小白色区域使用改进的膨胀腐蚀法进行图像补偿。实验表明，网幕图像识别精度在85%以上，单幅图像处理时间小于40ms。

Abstract:

According to color distribution characteristics of Hlyphantria cunea larva nets, RGB color space was selected and the data differences of each channel were analyzed for net curtains, leaves and branches. Furthermore, R—B color model with the Otsu method and threshold algorithm were used to segment images. The region labeling and Freeman coding methods were adopted to calculate the area of each region. The double threshold value was determined and residual noise was removed by using the mean and standard deviation of a plurality area. According to the differences between area sizes, fine white and white regions were compensated by using improved expansion corrosion method. Experimental results showed that the accuracy of net curtain image recognition was above 85% and single image processing time was less than 40ms.