基于CNN网络和多任务损失函数的实时叶片识别

针对传统叶片识别易受环境干扰,难以实现复杂背景下的多叶片实时识别问题,提出一种基于CNN网络和多任务损失函数的实时叶片识别方法。采用CNN网络提取叶片图像特征图,输入到RPN网络生成区域候选框;依据特征图和区域候选框,提取候选框特征图,分别进行叶片分类和边界框回归,预测叶片类别和叶片预测框的定位;利用多任务损失函数约束分类和回归,来提高叶片分类和回归的准确率和运算速度。实验结果表明,该方法的平均实时叶片识别准确率为91.8%,平均实时识别速度为25 fps。

Real-time Leaf Recognition Method Based on CNN Network and Multi-task Loss Function

Aiming at the problems that the traditional leaf recognition is susceptible to the environmental interference and is difficult to realize the multi-leaf real-time recognition in complex background, a real-time leaf recognition method based on CNN network and multi-task loss function is proposed. The CNN network is used to the extract image feature maps and input them into RPN network to generate regional proposals. On the basis of the feature maps and region proposals, the feature map is proposaled, the leaf classification and bounding box regression are performed respectively, and the leaf classification and position of the leaf prediction box are predicted. The multi-task loss function is used to constrain the classification and regression to improve the accuracy and speed of the leaf image classification and regression. Experimental results show that the average real-time leaf recognition accuracy is 91.8%, and the average real-time leaf recognition speed is 25 fps.