食用菌学报2025，Vol.32Issue(1)：89-101,13.DOI:10.16488/j.cnki.1005-9873.2025.01.010
基于YOLOv5s-GCE模型检测糙皮侧耳黄斑病
YOLOv5s-GCE Model-Based Detection of Yellow Spot Disease in Pleurotus ostreatus
摘要
Abstract
For accurate and efficient detection of yellow spot disease in Pleurotus ostreatus,a model named YOLOv5s-GCE was developed based on the YOLOv5s model.YOLOv5s-GCE integrated a lightweight GhostNet structure,embedded a coordinate attention(CA)module into the YOLOv5s backbone,and substituted the original CIOU loss function of YOLOv5s with the enhanced intersection over union(EIOU)loss function.Using a self-built yellow spot disease dataset,ablation and comparison experiments were conducted on YOLOv5s-GCE.Subsequently,the model was deployed on an RK3588S AI development board for validation.The results showed that YOLOv5s-GCE outperformed YOLOv5s in terms of mean average precision(mAP)(92.7％,2.7％increase over the baseline),complexity(significantly reduced),parameter count(decreased by 44.7％),model size(decreased by 43.4％),and computational cost(decreased by 47.2％in giga floating-point operations per second,GFLOPs).The overall performance of YOLOv5s-GCE was superior to other typical object detection models,such as SSD,YOLOv7,YOLOv8n,and Faster R-CNN.The detection speed of YOLOv5s-GCE deployed on RK3588S development board was 30.49 frames per second with an mAP value of 90.2％,which satisfied requirements of real-time detection of P.ostreatus yellow spot disease.The results provided a reference for subsequent development of intelligent devices for detecting pathogenic diseases in edible fungi.关键词
糙皮侧耳/黄斑病/YOLOv5s/目标检测Key words
Pleurotus ostreatus/yellow spot disease/YOLOv5s/object detection引用本文复制引用
张志勇,武同辉,刘靖宇,王硕,王宸,张燕青,郑维..基于YOLOv5s-GCE模型检测糙皮侧耳黄斑病[J].食用菌学报,2025,32(1):89-101,13.基金项目
山西省现代农业产业技术体系建设专项(2024CYJSTX09) （2024CYJSTX09）
山西省研究生教育创新项目(2022Y339) （2022Y339）
