农业机械学报2023，Vol.54Issue(z1)：222-229,8.DOI:10.6041/j.issn.1000-1298.2023.S1.023
基于YOLO v5s的作物叶片病害检测模型轻量化方法
Lightweight Method for Crop Leaf Disease Detection Model Based on YOLO v5s
摘要
Abstract
In order to effectively lightweight the leaf disease detection model under the premise of ensuring the recognition performance,a model lightweight method was constructed based on trunk replacement,model pruning and knowledge distillation technology,and a lightweight test was carried out on the leaf yellow leaf curl disease detection model based on YOLO v5s.Firstly,the main body of the model was reduced by replacing the YOLO v5s trunk with the common lightweight convolutional neural networks(LCNN)with excellent performance.Then,the unimportant channels were screened and deleted by using the sparse training of the model and the distribution of the scaling factors in the batch normalization layer.Finally,by fine-tuning retraining and knowledge distillation,the model accuracy was adjusted to a level close to that before pruning.The experimental results showed that the accuracy,recall and mean average accuracy of the lightweight model were 91.3％,87.4％and 92.7％,respectively.The memory consumption of the model was 1.4 MB,and the detection frame rate of the desktop was 81.0 f/s.The detection frame rate of the mobile terminal was 1.2 f/s.Compared with the original YOLO v5s leaf disease detection model,the accuracy,recall and average accuracy were reduced by 3.7 percentage points,4.6 percentage points and 2.7 percentage points,and the memory consumption was only 10％of that before processing.The frame rate of the desktop and mobile terminal detection was increased by nearly 27％and 33％,respectively.The proposed method can effectively reduce the weight of the model under the premise of keeping the performance,which provided a theoretical basis for the deployment of mobile leaf disease detection.关键词
病害检测/YOLO v5s/轻量化模型/网络剪枝/知识蒸馏Key words
disease detection/YOLO v5s/lightweight model/network pruning/knowledge distillation分类
信息技术与安全科学引用本文复制引用
杨佳昊,左昊轩,黄祺成,孙泉,李思恩,李莉..基于YOLO v5s的作物叶片病害检测模型轻量化方法[J].农业机械学报,2023,54(z1):222-229,8.基金项目
国家重点研发计划项目(2022YFD1900801) （2022YFD1900801）
