高精度轻量级的无人机航摄图像目标检测模型研究
High-Precision and Lightweight Object Detection Model for Drone Aerial Photography Images
赵向阳 1史再峰 2王云峰 1牛孝伟 1罗韬3
作者信息
- 1. 天津大学微电子学院,天津 300072
- 2. 天津大学微电子学院,天津 300072;天津市成像与感知微电子技术重点实验室,天津 300072
- 3. 天津大学智能与计算学部,天津 300072
- 折叠
摘要
针对当前主流的轻量级目标检测模型在无人机航摄场景下检测精度低的问题,提出一种基于YOLOv8s的高精度轻量级航摄图像目标检测模型LEFE-YOLOv8.首先设计内嵌注意力机制的增强特征提取卷积(EFEConv),其与部分通道卷积(PConv)及1×1卷积共同组成轻量级增强特征提取模块,在提高模型特征提取能力的同时降低了参数量与计算量.然后在特征融合网络中引入轻量级动态上采样算子模块,有效地解决了高层特征网络在上采样过程中的信息丢失问题.最后构建带有多尺度模块的检测头,提高了网络模型的多尺度检测能力.最终的实验结果表明,相较于基准模型,所提改进模型在VisDrone2019和HIT-UAV数据集上的平均精度分别达42.3%与83.9%,且参数量在10×106以下,更加适合航摄图像目标检测任务.
Abstract
The current mainstream lightweight object detection models exhibit low detection accuracy in unmanned aerial vehicle(UAV)photography scenes.This study introduces a high-precision and lightweight aerial photography image object detection model based on YOLOv8s,named LEFE-YOLOv8.First,an enhanced feature extraction convolution(EFEConv)incorporating an attention mechanism was developed.It is integrated with partial channel convolution(PConv)and 1×1 convolution to create a lightweight enhanced feature extraction module.This integration augments the model's feature extraction capabilities and reduces the number of parameters and computational complexity.Subsequently,a lightweight dynamic upsampling operator module was incorporated into the feature fusion network,effectively addressing the information loss problem during the upsampling process in high-level feature networks.Finally,a detection head with multi-scale modules was designed to enhance the network model's multi-scale detection capabilities.The final experimental results demonstrate that,compared with the benchmark model,the improved model achieves an average accuracy of 42.3%and 83.9%on the VisDrone2019 and HIT-UAV datasets,respectively,with less than 10×106 parameters.These results establish the model's suitability for aerial image object detection tasks.
关键词
目标检测/无人机航摄图像/增强特征提取/动态上采样/多尺度模块Key words
object detection/drone aerial photography image/enhanced feature extraction/dynamic upsampling/multiscale module引用本文复制引用
出版年
2024
NETL
NSTL
万方数据