Yu Ruonan, Li Hongguang, Jiang Yalong, Zhang Baochang, Wang Yufeng
School of Electrical and Information Engineering, Beihang University, Beijing 100191, China.
Unmanned System Research Institute, Beihang University, Beijing 100191, China.
Sensors (Basel). 2022 Mar 18;22(6):2354. doi: 10.3390/s22062354.
Mid-to-high altitude Unmanned Aerial Vehicle (UAV) imagery can provide important remote sensing information between satellite and low altitude platforms, and vehicle detection in mid-to-high altitude UAV images plays a crucial role in land monitoring and disaster relief. However, the high background complexity of images and limited pixels of objects challenge the performance of tiny vehicle detection. Traditional methods suffer from poor adaptation ability to complex backgrounds, while deep neural networks (DNNs) have inherent defects in feature extraction of tiny objects with finite pixels. To address the issue above, this paper puts forward a vehicle detection method combining the DNNs-based and traditional methods for mid-to-high altitude UAV images. We first employ the deep segmentation network to exploit the co-occurrence of the road and vehicles, then detect tiny vehicles based on visual attention mechanism with spatial-temporal constraint information. Experimental results show that the proposed method achieves effective detection of tiny vehicles in complex backgrounds. In addition, ablation experiments are performed to inspect the effectiveness of each component, and comparative experiments on tinier objects are carried out to prove the superior generalization performance of our method in detecting vehicles with a limited size of 5 × 5 pixels or less.
中高空无人机（UAV）图像能够在卫星和低空平台之间提供重要的遥感信息，并且中高空无人机图像中的车辆检测在土地监测和救灾中发挥着关键作用。然而，图像的高背景复杂性和物体像素有限对微小车辆检测的性能提出了挑战。传统方法对复杂背景的适应能力较差，而深度神经网络（DNN）在有限像素微小物体的特征提取方面存在固有缺陷。为了解决上述问题，本文提出了一种针对中高空无人机图像的结合基于DNN方法和传统方法的车辆检测方法。我们首先利用深度分割网络来挖掘道路和车辆的共生关系，然后基于具有时空约束信息的视觉注意力机制检测微小车辆。实验结果表明，该方法能够在复杂背景下有效检测微小车辆。此外，进行了消融实验以检验各组件的有效性，并对更小的物体进行了对比实验，以证明我们的方法在检测尺寸为5×5像素及以下的有限大小车辆时具有卓越的泛化性能。
