现代电子技术2024，Vol.47Issue(24)：60-67,8.DOI:10.16652/j.issn.1004-373x.2024.24.010
改进YOLOv5的棉田杂草检测
Improved YOLOv5 detection of weeds in cotton fields
摘要
Abstract
In allusion to the difficulty of detecting and identifying weeds in cotton fields in complex environments,a cotton field weed detection algorithm CST-YOLOv5 is proposed to improve YOLOv5.The data enhancement algorithm is used to solve the problem of insufficient model training effect due to the unbalanced distribution of weed samples in cotton fields.A coordinate attention mechanism is added to the backbone network by considering channel information and direction location information.The Swin Transformer Block is introduced into the C3 module in the neck network to obtain a new C3STR module to preserve global context information and multi-scale features.The experimental results show that the mAP value of the CST-YOLOv5 model can reach 95.1%,and the F1 value can reach 90.4%,which are respectively increased by 4.8%and 3.2%compared with the original YOLOv5 model.It verifies that the designed algorithm has good robustness and can accurately identify many types of weeds.关键词
杂草检测/YOLOv5/深度学习/目标检测/注意力机制/棉花保护Key words
weed detection/YOLOv5/deep learning/target detection/attention mechanism/cotton protect分类
电子信息工程引用本文复制引用
杨明轩,陈琳..改进YOLOv5的棉田杂草检测[J].现代电子技术,2024,47(24):60-67,8.基金项目
国家自然科学基金项目(62006028) （62006028）
湖北省自然科学基金项目(2022CFB132) （2022CFB132）
湖北省教育厅自然科学研究计划项目(B2022038) （B2022038）
