Robust vehicle detection in high-resolution aerial images with imbalanced data

Vehicle detection in images from unmanned aerial vehicles (UAVs) plays an important role in traffic surveillance and urban planning due to the popularity of UAVs. However, the class imbalance problem is an important factor that restricts the performance of vehicle detectors. There are two types of class imbalance in UAV images, i.e., foreground-background imbalance and foreground-foreground imbalance. For anchor-based single stage detector, as many ground truths cannot be assigned to corresponding anchors because of low intersection over union (IoU), it makes the foreground-background imbalance problem more severe. Therefore, we propose a novel Bag-based Single-Stage Detector which treats each position on the feature map as a bag. A simple and adaptive definition of bags is proposed along with the positive sample definition method which is utilized to ensure more ground truths can be assigned to proper bags. In addition, we utilize online hard example mining (OHEM) method to control the proportion of positive and negative samples during the training process. To address the foreground-foreground imbalance, we propose a novel data augmentation algorithm which allows us to create appropriate visual context for under-represented class. Extensive experiments demonstrate the superiority of the proposed algorithm, compared with other state-of-the-art (SOTA) solutions