现代电子技术2026，Vol.49Issue(19)：10-15,6.DOI:10.16652/j.issn.1004-373X.2026.19.002
改进YOLOv11n的菌落图像检测算法
Enhanced YOLOv11n algorithm for bacterial colony detection in microscopic imaging
摘要
Abstract
In view of the miscounting and undercounting caused by small object sizes,high density,adhesion,clumping,and overlapping in bacterial colony image detection,this paper proposes an improved algorithm named YOLOv11n-DPD based on the YOLOv11n model,aiming to enhance detection accuracy for small object colonies.Firstly,the C3k2 module in the Backbone is replaced with the C2f_Dense module to strengthen feature reuse and model representation capabilities for small object colonies.Secondly,a P2 small object detection layer is added to improve feature integration across hierarchical levels and multi-scale feature fusion,thereby boosting the extraction of small object colony features.Finally,a dynamic detection head is adopted to replace the original decoupled detection head,which effectively integrates multi-scale information and further enhance detection accuracy for small object colonies.Experiments on the Roboflow colony dataset show that,in comparison with YOLOv11n,YOLOv11n-DPD exhibits significant improvements in all relevant metrics;its precision,recall rate,mAP@0.5,and mAP@0.5:0.95 are increased by approximately 12.5%,15.5%,14.2%,and 6.8%,respectively.The YOLOv11n-DPD exhibits strong practical applicability for bacterial colony image detection.关键词
小目标检测/菌落图像/YOLOv11n/深度学习/YOLOv11n-DPD/多尺度信息Key words
small object detection/bacterial colony image/YOLOv11n/deep learning/YOLOv11n-DPD/multi-scale information分类
信息技术与安全科学引用本文复制引用
郭梦凡,杨天羽,王刘玺,陆利霞,熊晓辉,崔晓文..改进YOLOv11n的菌落图像检测算法[J].现代电子技术,2026,49(19):10-15,6.基金项目
江苏省"双创博士"项目(2020)30539 （2020）
南京工业大学引进人才科研启动专项经费资助项目 （）
南京工业大学人工智能类智慧课程项目基金 （）
