Segmentation of peanut leaf spot instances based on improved Cascade Mask R-CNN
Aiming at the problem of complex background of peanut leaf images in natural environment,which leads to poor detection effect,a method based on the improved Cascade fc-Mask R-CNN instance segmentation model is proposed to study the lesion segmentation method of peanut leaves in natural environment in the field,taking peanut as the research object.Firstly,the Cascade Mask R-CNN model is built based on the peanut leaf spot segmentation dataset,secondly,the model is improved by replacing the original backbone network with a combination of ResNext101 and feature pyramid,and the loss function is adjusted,and then the mask branch is improved to construct the Cascade fc-Mask R-CNN model.The labeled peanut leaf spot segmentation dataset was input into different segmentation network models for training and validation,and after a series of experiments,the results showed that the improved Cascade fc-Mask R-CNN model achieved 98.9％accuracy,77.5％bounding box regression accuracy,and 77.9％segmentation accuracy.Compared with other segmentation models,the improved Cascade fc-Mask R-CNN model has the best instance segmentation recognition on the peanut leaf spot segmentation dataset.
deep learningmachine visioninstance segmentationpeanut disease
万方数据
维普
