摘要
针对小尺寸松材线虫病受害木检测精度及检测效率低的问题,提出了一种融合深度网络和注意力机制的小尺寸松材线虫智能检测模型。采用无人机(UAV)搭载小型相机在220 m高度拍摄小尺寸松材线虫受害木图像,应用图像旋转、缩放、添加高斯噪声和模拟光照强度等数据处理方式扩充数据集,设计轻量级深度网络NanoDet和SimAM注意力模块融合模型NanoDet-SimAM对小尺寸松材线虫受害木进行精准检测。结果表明,该模型相较于Faster R-CNN、Yolov4、Yolov5s及NanoDet等检测网络模型,具有更高的检测精度、速度和稳定性。
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.
作者
刘芳
姜生伟
张峻豪
何姗
LIU Fang;JIANG Shengwei;ZHANG Junhao;HE Shan(School of Science,Shenyang Ligong University,Shenyang 110159,Liaoning,China;Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry,Shenyang Ligong University,Shenyang 110159,Liaoning,China;School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,Liaoning,China;Key Laboratory of Nation Forestry and Grassland Administration on Northeast Area Forest and Grass Dangerous Pest Management and Control,Liaoning Forestry and Grassland Bureau,Shenyang 110036,Liaoning,China;Key Laboratory of Nation Forestry and Grassland Administration on Northeast Area Forest and Grass Dangerous Pest Management and Control,Shenyang Institute of Technology,Shenyang 113122,Liaoning,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2024年第4期428-433,共6页
Journal of Shenyang University of Technology
基金
辽宁省民生科技计划项目(2021JH2/10200008)
辽宁省“兴辽人才”项目(XLYC2006017)
辽宁省教育厅基本科研项目(LJKMZ20220619)。
