Clustered Object Detection in Aerial Images
Clustered Object Detection in Aerial Images
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DOI：
10.1109/iccv.2019.00840
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Fan Yang;Heng Fan;Peng Chu;Erik Blasch;Haibin Ling
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文献类型：
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作者：
Fan Yang;Heng Fan;Peng Chu;Erik Blasch;Haibin Ling
检测航拍图像中的对象是具有挑战性的，原因至少有两个：（1）像行人这样的目标对象的像素非常小，使得它们很难与周围背景区分开;以及（2）目标通常稀疏且不均匀地分布，使得检测非常低效。在本文中，我们解决这两个问题的启发，观察这些目标往往是集群。特别是，我们提出了一个统一的对象聚类和检测在一个端到端的框架中的检测网络（WIDET）。CMDet中的关键组件包括集群建议子网络（CPNet），规模估计子网络（ScaleNet）和专用检测网络（DetecNet）。给定一个输入图像，CPNet生成对象聚类区域，ScaleNet估计这些区域的对象尺度。然后，每个尺度归一化的聚类区域被送入DetecNet进行对象检测。与以前的解决方案相比，WARDET具有以下几个优点：（1）它大大减少了用于最终目标检测的芯片数量，因此实现了高运行时间效率，（2）基于簇的尺度估计比先前使用的基于单个目标的尺度估计更准确，因此有效地改进了对小目标的检测，最后的DetecNet是专门用于聚类区域的，并隐式地对先验上下文信息进行建模，以提高检测精度。该方法在VisDrone、UAVDT和DOTA三种流行的航空影像数据集上进行了测试。在所有的实验中，与最先进的检测器相比，DET实现了令人鼓舞的性能。
Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues inspired by observing that these targets are often clustered. In particular, we propose a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet). Given an input image, CPNet produces object cluster regions and ScaleNet estimates object scales for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has several advantages over previous solutions: (1) it greatly reduces the number of chips for final object detection and hence achieves high running time efficiency, (2) the cluster-based scale estimation is more accurate than previously used single-object based ones, hence effectively improves the detection for small objects, and (3) the final DetecNet is dedicated for clustered regions and implicitly models the prior context information so as to boost detection accuracy. The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet achieves promising performance in comparison with state-of-the-art detectors.