沈阳农业大学学报2023，Vol.54Issue(6)：702-711,10.DOI:10.3969/j.issn.1000-1700.2023.06.007
基于改进Mask-RCNN的桃树穿孔病检测研究
Study on Peach Shot-hole Disease Detection Based on Improved Mask-RCNN
摘要
Abstract
Perforation disease is a common disease of peach trees,which is divided into bacterial shot-hole disease(BSD)and fungal shot-hole disease(FSD).Most fruit growers have difficulty in accurately identifying them through their experience,which leads to mistaken prevention and control,resulting in yield reduction.To solve this problem,a peach tree perforation disease detection method based on Mask RCNN(mask region based convolutional neural network)was proposed.The method improved the Mask RCNN model in three aspects:firstly,the Sim-AM(simple,parameter-free attention module)mechanism was integrated into each layer of the residual network,and the energy function was used to assign three-dimensional weights to the neurons,which enhanced the extraction of key features of the perforation disease;secondly,for RPN network Repeated computation of recognition boxes,a simplification process was carried out to reduce the number of Anchor Boxes from nine to three,which improved the computational efficiency;again,the NMS algorithm was replaced with the Soft Non-maximum Suppression algorithm(Soft-NMS,soft non-maximum suppression)for the Anchor Box selection,which improved the detection of occluded diseased spots.The study used the secondary migration learning method to train the model,firstly using the publicly available apple leaf perforation disease dataset on the Kaggle platform for training and learning,and then using the feature that the two datasets have a similar feature space,the secondary migration learning was carried out on the self-constructed peach tree perforation disease dataset,which improved the detection accuracy.The experimental results showed that the improved Mask-RCNN model achieved an average detection accuracy mAP of 94.1%and a recall rate of 93.5%for all categories of peach tree perforation disease.关键词
桃树穿孔病/Msak RCNN/Sim-AM/迁移学习Key words
peach tree perforation disease/Msak RCNN/Sim-AM/transfer learning分类
农业科技引用本文复制引用
胡彦军,张平川,张彩虹,陈旭,李珊,杨莹,马泽泽..基于改进Mask-RCNN的桃树穿孔病检测研究[J].沈阳农业大学学报,2023,54(6):702-711,10.基金项目
河南省科技厅科技攻关项目(222102210116) （222102210116）
