Real-time segmentation of plant leaves based on sparse instances and posi-tion aware convolution
The segmentation of plant leaves plays a crucial role in high-throughput plant phenotyping data acquisition tasks.Currently,most methods for plant leaf segmentation focus on improving the accuracy of the segmentation model but o-verlook the model's complexity and inference speed.In response to this issue,this study proposed an instance segmentation model(ePaCC-SparseInst)based on sparse instance activation and efficient position-aware convolution to achieve real-time and accurate segmentation of plant leaves.In ePaCC-SparseInst,a set of sparse instance activation maps was introduced as the representation of leaf objects.A bipartite graph matching algorithm was employed to establish a one-to-one mapping be-tween predicted objects and instance activation maps,thereby avoiding the cumbersome non-maximum suppression(NMS)operation and improving the model's inference speed.Additionally,an effective position-aware circulate convolution(ePaCC)module was introduced into the instance branch,which increased the model's global receptive field and enhanced its inference speed.On the Komatsu-na dataset,ePaCC-SparseInst achieved an average segmentation precision(AP)of 85.33%and an inference speed of 43.52 frames per second(FPS).Under the same training conditions,its performance surpassed instance segmentation al-gorithms such as SparseInst,Mask R-CNN,and CondInst.Furthermore,on the CVPPP A5 dataset,ePaCC-SparseInst a-chieved better segmentation accuracy and inference speed than the aforementioned algorithms.The proposed method used a pure convolutional architecture to achieve real-time leaf segmentation,which could provide technical support for obtaining plant phenotypic data on mobile or edge devices.
NETL
NSTL
万方数据
维普
