为了提高复杂背景植物图像识别准确率,提出一种基于卷积神经网络(CNN)有效区域筛选的植物图像识别方法,该方法首先基于图像(花朵、叶片)数据集利用CNN训练一个有效区域筛选模型,使数据集通过该模型筛选后仍能保留花朵、叶片等有效区域;然后经过Mask R-CNN对植物图像数据集进行有效区域的提取,再用有效区域筛选模型筛选能表征植物图像类别的有效区域,接着将此类有效区域以4∶1的比例划分为训练集和测试集,然后送入GoogleNet进行训练,得到基于有效区域的CNN植物图像识别模型MRC-GoogleNet;最后通过该模型得出识别准确率。实验结果和数据表明,与经典CNN植物图像识别模型相比,基于有效区域筛选的识别模型能提取到更为有效的图像特征,有效地提高识别准确率。

A plant image recognition method, which is based on effective region screening through a convolutional neural network (CNN), is proposed with an aim to improve the accuracy of plant image recognition in complex backgrounds. First, image (flower, leaf) datasets are used to train an effective region-screening model through a CNN, which is designed to allow the datasets to retain effective areas such as flowers and leaves after screening through the model. Subsequently, the effective areas are extracted from the plant image data sets by Mask R-CNN. Then the effective area screening model is used to screen the effective areas that can represent the plant image categories. The effective areas are divided into training sets and test sets in a ratio of 4∶1. The CNN plant image recognition model based on effective region selection (MRC-GoogleNet) is obtained after training in GoogleNet. Finally, the recognition accuracy is obtained through the model. The experimental results and data reveal that the recognition model, which is based on effective region selection, can more effectively extract image features and improve the recognition accuracy compared with the classical CNN plant image recognition model.