论文标题
sa-net.v2：在深度元学习中使用不确定性估算的实时车辆检测
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning
论文作者
论文摘要
近年来，无人机（UAV）成像是在城市规模上实时监视不同车辆的合适解决方案。通过使用小型培训数据集的可移植平台（例如，无人机）使用不确定性估计的实时车辆检测可能会改善现实世界中应用程序中的视频理解，而许多车辆监控方法似乎使用大型培训数据集了解单时间检测。从倾斜无人机图像中实时车辆检测的目的是通过使用语义分割在时间序列的无人机图像上找到车辆。由于斜视图像中的各种深度和比例车辆的多样性，实时车辆检测更加困难。在这些事实的激励中，在本手稿中，我们考虑了基于小型训练数据集和深度元学习的斜uav图像的实时车辆检测问题。所提出的称为SA-NET.V2的构建是一种基于SA-CNN的开发方法，该方法通过重新设计挤压和注意机制，用于实时车辆检测。 sa-net.v2由两个组件组成，包括根据小型训练数据集提取高级功能的挤压和注意功能，以及封闭的CNN。对于实时车辆检测方案，我们在无人机数据集上测试我们的模型。无人机是一个时间序列的斜无人机图像数据集，该数据集由30个视频序列组成。我们使用时间序列无人机图像检查了所提出的方法在城市环境中实时车辆检测的适用性。实验表明，sa-net.v2在时间序列的斜体无人机图像中实现了有希望的表现。
In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution for real-time monitoring different vehicles on the urban scale. Real-time vehicle detection with the use of uncertainty estimation in deep meta-learning for the portable platforms (e.g., UAV) potentially improves video understanding in real-world applications with a small training dataset, while many vehicle monitoring approaches appear to understand single-time detection with a big training dataset. The purpose of real-time vehicle detection from oblique UAV images is to locate the vehicle on the time series UAV images by using semantic segmentation. Real-time vehicle detection is more difficult due to the variety of depth and scale vehicles in oblique view UAV images. Motivated by these facts, in this manuscript, we consider the problem of real-time vehicle detection for oblique UAV images based on a small training dataset and deep meta-learning. The proposed architecture, called SA-Net.v2, is a developed method based on the SA-CNN for real-time vehicle detection by reformulating the squeeze-and-attention mechanism. The SA-Net.v2 is composed of two components, including the squeeze-and-attention function that extracts the high-level feature based on a small training dataset, and the gated CNN. For the real-time vehicle detection scenario, we test our model on the UAVid dataset. UAVid is a time series oblique UAV images dataset consisting of 30 video sequences. We examine the proposed method's applicability for stand real-time vehicle detection in urban environments using time series UAV images. The experiments show that the SA-Net.v2 achieves promising performance in time series oblique UAV images.
