基于改进YOLOv5s的苹果叶片小目标病害轻量化检测方法OACSCDCSTPCD
Lightweight detection of small target diseases in apple leaf using improved YOLOv5s
为解决自然环境中苹果叶片病害检测场景复杂、小目标病害检测难度高以及模型参数大无法在移动端和嵌入式设备部署等问题,提出一种基于YOLOv5s的苹果叶片小目标病害轻量化检测方法.该方法将YOLOv5s的骨干网络更改为ShuffleNet v2轻量化网络,引入CBAM(convolutional block attention module)注意力模块使模型关注苹果叶片小目标病害,添加改进RFB-s(receptive field block-s)支路…查看全部>>
Leaf diseases have seriously threatened the quality and yield of apple fruits.Efficient and accurate identification of apple leaf diseases is of great significance for refined orchard management.However,it is difficult to detect apple leaf disease at the early stage,due to the small target disease lesion and complex scenarios.Furthermore,the parameters of the detection model are too large to deploy on mobile terminals or embedded devices.In this study,a ligh…查看全部>>
公徐路;张淑娟
山西农业大学农业工程学院,太谷 030801||山西农业大学软件学院,太谷 030801山西农业大学农业工程学院,太谷 030801
计算机与自动化
病害深度学习目标检测苹果叶片YOLOv5s
diseasedeep learningobject detectionapple leafYOLOv5s
《农业工程学报》 2023 (19)
175-184,10
山西省重点研发项目(No.201903D221027)教育部产学合作协同育人项目(No.220504309235356)
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