首页|轻型注意力卷积无人机影像车辆检测模型研究

轻型注意力卷积无人机影像车辆检测模型研究

扫码查看
原文链接
  • 万方数据
  • 维普
提出一种能够部署于无人机终端的轻量级端到端车辆检测模型.在骨干网络中,首先,使用焦点机制对输入的原始图像进行无损下采样;然后,利用带有轻型注意力模块的深度可分离卷积核组成特征提取层;最后,在特征金字塔中通过跨尺度多层融合来提高三个层级输出特征图内的信息复杂程度.将开源无人机影像数据集VisDrone与多个时期采集的无人机道路影像混合,经过增强处理后作为训练集对模型进行训练.实验结果表明,本文所提出模型对于各类车辆目标均表现出稳定的检测性能,在综合检测精度方面明显优于几组对照模型,同时训练后模型体量较小,能够在测试环境的嵌入式硬件终端上部署并开展实时检测.
Research on Vehicle Detection Model of Lightweight Attention Convolution UAV Image
This paper proposes a lightweight end-to-end vehicle detection model which can be deployed on UAV terminals. In the backbone network, the focus mechanism was first used to down sample the input original image losslessly, and then the depthwise sep-arable convolution kernel with a light attention module was used to established the feature extraction layer, finally the multi-layer fu-sion across scales is performed in the feature pyramid to improve the information complexity in the output feature maps of the three lev-els. The open-source UAV image dataset VisDrone is mixed with UAV road images collected in multiple periods, and the model was trained as a training set after enhanced processing. The experimental results show that the model proposed in this paper shows stable detection performance for all kinds of vehicle targets, and is significantly better than several groups of control models in terms of com-prehensive detection accuracy. At the same time, the volume of the model after training is small, and can be deployed and carried out real-time detection on the embedded hardware terminal of the test environment.

UAVvehicle detectionlightweight modelattention mechanismdepthwise separable convolution

杨秀伶

展开 >

重庆水利电力职业技术学院,重庆402160

无人机 车辆检测 轻量级模型 注意力机制 深度可分离卷积

重庆市教委科学技术研究计划重庆市教委高等职业教育教学改革项目重庆市高教学会高等教育研究课题重庆水利电力职业技术学院院级重点教学改革项目重庆水利电力职业技术学院院级科研项目(2021)

KJQN202103803Z213145CQGJ21A051202111

2024

测绘与空间地理信息
黑龙江省测绘学会

测绘与空间地理信息

影响因子：0.788
ISSN：1672-5867
年,卷(期)：2024.47(4)
  • 9