基于CS-YOLOv5s的无人机航拍图像小目标检测
Small target detection for UAV aerial images based on CS-YOLOv5s
翁俊辉 1成乐 2黄曼莉 1隋皓 1朱宏娜1
作者信息
- 1. 西南交通大学物理科学与技术学院 成都 610031
- 2. 西南交通大学信息科学与技术学院 成都 610031
- 折叠
摘要
无人机航拍图像存在小目标分布密集且目标尺度变化大等检测难点,本文提出一种面向无人机航拍图像小目标的跨尺度目标检测模型—CS-YOLOv5s.首先,在YOLOv5s基础上,引入小目标检测器,提高模型对小目标的捕捉能力;进一步,将最大池化分支嵌入上下文增强模块,提取并增强骨干网络尾部的深层特征,再注入PANet,实现深浅层特征有效融合和模型跨尺度检测能力的提升;同时采用SPDConv模块替换下采样卷积模块,实现无人机航拍图像中密集目标高效检测.实验表明,CS-YOLOv5s在数据集VisDrone2019达到42.0％mAP0.5,较基准模型提升9.8％,有效增强网络模型对无人机航拍图像小目标的识别能力,为无人机目标智能识别提供支撑.
Abstract
To address the challenges in detecting small targets with dense distribution and large-scale variations in UAV aerial images,a cross-scale target detection model for UAV aerial images,named CS-YOLOv5s,is proposed.Firstly,based on YOLOv5s,micro-object detector is utilized to improve the model ability for capturing small targets.Then,the max-pooling branch is embedded into the context augment model,extracting and enhancing deep feature maps at the tail of the backbone network.The PANet is injected to achieve effective fusion of deep and shallow features with enhancing the cross-scale detection capability.Furthermore,the down-sampling convolution module is replaced with the SPDConv module to achieve efficient detection of dense objects in UAV aerial images.Experiments demonstrate that CS-YOLOv5s achieves 42.0％mAP0.5 on the VisDrone2019 dataset,which is increased by 9.8％than that of the baseline model.Our model enhances the network ability to recognize small targets in UAV aerial images effectively,which provides a new way for intelligent targets recognition of UAV.
关键词
无人机航拍图像/YOLO/小目标检测器/上下文增强模块/SPDConv模块Key words
UAV aerial images/YOLO/micro-object detector/context augmentation module/SPDConv module引用本文复制引用
出版年
2024
NETL
NSTL
万方数据