Investigating the Challenges of Class Imbalance and Scale Variation in Object Detection in Aerial Images
This work addresses object detection problems for aerial imagery applications, but it is incremental as it builds on existing methods with specific tweaks.
The paper tackles object detection in aerial satellite images by modifying the Faster-RCNN architecture to address challenges like scale variation and class imbalance, achieving a 4.7 mAP improvement over the baseline.
While object detection is a common problem in computer vision, it is even more challenging when dealing with aerial satellite images. The variety in object scales and orientations can make them difficult to identify. In addition, there can be large amounts of densely packed small objects such as cars. In this project, we propose a few changes to the Faster-RCNN architecture. First, we experiment with different backbones to extract better features. We also modify the data augmentations and generated anchor sizes for region proposals in order to better handle small objects. Finally, we investigate the effects of different loss functions. Our proposed design achieves an improvement of 4.7 mAP over the baseline which used a vanilla Faster R-CNN with a ResNet-101 FPN backbone.