Object Detection in Aerial Images: What Improves the Accuracy?
This work addresses object detection for aerial imagery, but it is incremental as it adapts an existing method with specific improvements.
The paper tackled the problem of object detection in aerial images, which is challenging due to scale and viewpoint variations, by investigating strategies to improve Faster R-CNN, resulting in a 4.96% mAP gain on the iSAID validation set.
Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.