Real-Time Leaf Recognition Method Based on Image Segmentation and Feature Extraction

Abstract

Leaf recognition has been an important research field of image recognition in the recent past. However, traditional leaf recognition methods can be easily affected by environments and cannot realize multi-leaf recognition under a complex background in real time. In this work, we present a real-time leaf recognition method based on image segmentation and feature recognition. First, we denoise the input of a leaf image, performing a leaf segmentation with an improved FCN network model, and then optimize the contour edge with a CRF algorithm to get a leaf segmentation image. Second, we extract the content features of the segmented leaf image with an Inception-V2 network model to get a feature map of the leaf image. Third, we input the feature map into an RPN network to obtain a set of regional candidate frames and then integrate the feature map and the information of candidate frames in a RoI Pooling layer, which can extract the feature map of a candidate frame area and scale it to a fixed-size feature map. Finally, we send the feature map to a fully connected layer to classify each preselection box content through the calculation of preselection feature maps, and then obtain the final accurate position of the prediction box by utilizing a bounding box regression. The experimental results show that the proposed method can achieve multi-leaf recognitions with high accuracy and fast speed under complex environments in real time.