Detection based on NanoDet-SimAM for small-size trees with pine wilt disease
LIU Fang
JIANG Shengwei
ZHANG Junhao
HE Shan
Abstract：Aiming at the problems of lower precision and efficiency in the detection of small-size pine wilt disease(PWD)injured trees,an intelligent detection model of small-size PWD injured trees was proposed.This model combined depth network and attention mechanism.A small camera equipped with an unmanned aerial vehicle(UAV)was used to capture images of small-size PWD injured tree at the height of 220 m.Data processing methods were applied to expand the data set.The processing methods included image rotation,scaling,adding Gaussian noise and simulating light intensity.A lightweight deep network NanoDet and a SimAM attention module fusion model NanoDet-SimAM were designed to realize accurate detection of small-size PWD injured trees.The results show that the model has higher detection accuracy,speed and stability than Faster R-CNN,Yolov4,Yolov5s and NanoDet,etc.
Keywords：pine wilt diseasetarget detectionlightweight network NanoDetattention mechanismnon-parametric attentiontransfer learningdata enhancementsmall size
Publication Date：2024-07-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 428-433 )