Flower Recognition Based on Fusion Convolution Neural Network
Computer technologies can help people identify different kinds of plants quickly.In order to solve the difficult prob-lem of plants recognition under complex background,two kinds of flower data sets are selected as the research object with different background.Firstly,a variety of convolution neural networks are used to classify flowers with global features of flower images and find the best network.Secondly,this paper extracts effective regions of the plant image and removes the invalid regions in the image by using Mask R-CNN,which makes sure that the network following can get more accurate effective features.Finally,the best net-work is trained again with processed images.The results show that this method can improve the accuracy of plant recognition with simple background by 3%and in complex background by 5%.
国家科技期刊平台
NSTL
万方数据
