通信与信息技术Issue(2)：26-31,58,7.
基于改进YOLOv11的监控视频小目标检测算法
Small target detection algorithm in surveillance video based on improved YOLOv11
尹哲 1王广龙 1林森 1李婷雪 1张艳珠1
作者信息
- 1. 沈阳理工大学自动化与电气工程学院,辽宁 沈阳 110159
- 折叠
摘要
Abstract
In order to solve the problems of weak feature expression,high miss rate,large background interference and inaccurate re-gression of bounding box caused by image deformation in small target detection at long distance and high viewing angle in surveillance video,an improved target detection algorithm BEH-YOLOv11n is proposed.Firstly,the structure of C3K2 is replaced by MSBlock module to enhance the model's perception of targets of different scales.Secondly,HS-FPN network is introduced to replace the original neck net-work to improve the expression ability of small targets in high and low level feature maps;Finally,the attention mechanism of EMA is intro-duced into C2PSA,and the regression loss function of bounding box is replaced by CIoU,which enhances the modeling ability of the model for the spatial position and direction of small targets.The experimental results based on VisDrone2019 data set show that the accuracy of the improved algorithm is increased by 9 percentage points,the mAP50%is increased by 5.6%compared with the original algorithm,and the detection speed FPS is 112 frames/s.It meets the standards of real-time detection while maintaining accuracy and accuracy,and meets the requirements of small target detection in complex monitoring environment.关键词
小目标检测/YOLOv11/多尺度特征融合Key words
Small target detection/YOLOv11/Multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
尹哲,王广龙,林森,李婷雪,张艳珠..基于改进YOLOv11的监控视频小目标检测算法[J].通信与信息技术,2026,(2):26-31,58,7.
