Liu Chongyang, Ding Yalin, Zhu Ming, Xiu Jihong, Li Mengyang, Li Qihui
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2019 Jul 26;19(15):3294. doi: 10.3390/s19153294.
Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these issues. In the detection stage, we propose a novel algorithm to generate oriented proposals that could enclose the vehicle objects properly as rotated rectangles with orientations. To discriminate the object and background in the proposals, we propose a modified vector of locally aggregated descriptors (VLAD) image representation model with a recently proposed image feature, i.e., local steering kernel (LSK) feature. By applying non-maximum suppression (NMS) after classification, we show that each vehicle object is detected with a single-oriented bounding box. Experiments are conducted on aerial images to compare the proposed method with state-of-art methods and evaluate the impact of the components in the model. The results have proven the robustness of the proposed method under various circumstances and the superior performance over other existing vehicle detection approaches.
航空图像中的车辆检测在民用和军事应用中发挥着重要作用，并且面临许多挑战，包括俯视视角、高度复杂的背景以及车辆的变体。本文提出了一种强大的车辆检测方案来克服这些问题。在检测阶段，我们提出了一种新颖的算法来生成定向提议，这些提议能够将车辆对象恰当地包围起来，作为具有方向的旋转矩形。为了区分提议中的对象和背景，我们提出了一种改进的局部聚合描述符向量（VLAD）图像表示模型，并结合了最近提出的图像特征，即局部转向核（LSK）特征。通过在分类后应用非极大值抑制（NMS），我们表明每个车辆对象都能以单个定向边界框被检测到。在航空图像上进行了实验，将所提出的方法与现有方法进行比较，并评估模型中各组件的影响。结果证明了所提出方法在各种情况下的鲁棒性以及优于其他现有车辆检测方法的性能。
