A corn disease and pest detection method based on lightweight improved YOLOv5s
Aiming at the problems of unsatisfactory detection accuracy,complex model and difficult deployment on mobile terminals in the existing maize disease and pest detection methods in complex environments,this study proposed a maize disease and pest detection method based on lightweight improved YOLOv5s.Firstly,the lightweight network GhostNet was used to replace the convolutional layer in the feature extraction network and feature fusion network in the original YOLOv5s model,which reduced the calculation and parameter amount of the model and improved the running speed to meet the deployment requirements of the mobile terminal.Secondly,in order to compensate for the problem of detection accuracy degradation caused by GhostNet,the normalization-based attention module(NAM)was introduced into the backbone feature extraction network of the model to evaluate the feature weights more comprehensively,so as to enhance the characteristics of corn diseases and pests,weaken the inter-ference of irrelevant information,and improve the detection performance.Finally,the loss function of the model was re-placed by EIOU from CIOU to enhance the model's ability to accurately locate the target,so as to improve the conver-gence speed and regression accuracy of the model.The ex-perimental results showed that compared with the original YOLOv5s model,the P,R and mAP of the final improved model increased by 1.9 percentage points,2.2 percentage points and 2.0 percentage points,respectively,reaching 94.6%,80.2%and 88.8%.While maintaining high detection accuracy,the calculation amount,parameter amount and capacity of the model were reduced by 50.6%,52.9%and 50.4%,which solved the deployment problem of the detection model on the mobile terminal.
cornpests and diseasesdetection modelYOLOv5slightweight
NETL
NSTL
万方数据
维普
