江苏农业学报2025，Vol.41Issue(2)：323-334,12.DOI:10.3969/j.issn.1000-4440.2025.02.013
基于改进YOLOv8n模型的辣椒病害检测方法
A chili disease detection method based on an improved YOLOv8n model
摘要
Abstract
To solve the problems of slow detection speed,high missed detection rate,and high false detection rate in chili disease detection,this study used YOLOv8n as the baseline model and constructed an improved YOLOv8n model(YOLOv8n-ATA model).The Adown downsampling module was introduced to replace the convolutional downsampling layer of the Backbone of the original model.The SlimNeck module was introduced to replace the convolutional layer and feature aggre-gation module(C2f)in the neck network of the original model with the hybrid convolution module(GSConv)and the cross-stage partial network(VoVGSCSP)module.Moreover,the auxiliary training head Aux Head(Auxiliary head)was used to fuse the original detection head.Finally,the performance of the improved model was evaluated using the image datasets of four chili diseases,such as anthracnose,brown spot,blossom-end rot and bacterial leaf spot.The results showed that the floating-point calculations and size of the improved model were 19.5%and 10.2%higher than those of the original YOLOv8n model.However,the identification accuracy,mAP50 and mAP50∶95 of the model for chili diseases increased by 2.6 percentage points,2.9 percentage points and 2.9 percentage points,respectively.At the same time,the number of frames per second increased by 15.1%.Therefore,the improved model can effectively identify chili diseases and better achieve the balance between accu-racy and efficiency of model recognition.关键词
辣椒病害/YOLOv8n模型/目标检测/Adown下采样模块/SlimNeck模块/Aux Head检测头Key words
chili diseases/YOLOv8n model/tar-get detection/Adown downsampling module/SlimNeck module/Aux Head分类
农业科技引用本文复制引用
李芳,危疆树,王玉超,张尧,谢宇鑫..基于改进YOLOv8n模型的辣椒病害检测方法[J].江苏农业学报,2025,41(2):323-334,12.基金项目
四川省科技厅关键技术攻关项目(22ZDYF0095) （22ZDYF0095）
