Despite recent advances, object detection in aerial images is still a
challenging task. Specific problems in aerial images makes the detection
problem harder, such as small objects, densely packed objects, objects in
different sizes and with different orientations. To address small object
detection problem, we propose a two-stage object detection framework called
"Focus-and-Detect". The first stage which consists of an object detector
network supervised by a Gaussian Mixture Model, generates clusters of objects
constituting the focused regions. The second stage, which is also an object
detector network, predicts objects within the focal regions. Incomplete Box
Suppression (IBS) method is also proposed to overcome the truncation effect of
region search approach. Results indicate that the proposed two-stage framework
achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all
other state-of-the-art small object detection methods reported in the
literature, to the best of authors' knowledge.

本文提出了一种名为 “Focus-and-Detect” 的两阶段物体检测框架，包括一个用高斯混合模型监督的物体检测网络来生成聚焦区域，以及一个物体检测网络来预测指定区域中的物体。同时，本文还提出了一种名为 IBS 的方法，可以克服区域搜索方法的截断效应，并在 VisDrone 验证数据集上获得了 42.06 的 AP 得分，超越所有已知的最先进的小物体检测方法。