Object Detection in Aerial Images with Attention-based Regression Loss

Object detection is a computer vision technique used to identify objects that are usually present in natural scenes. However, the methods used for this case are not easily transferable to detect objects in aerial images. Objects in aerial images are mostly arbitrary-oriented, small, and in complex backgrounds compared to upright and well-focused objects in natural scenes. To effectively detect objects in aerial images, we propose a new regression loss function based on the attention mechanism through attention weights. Using the relative position of the attention weights to the bounding box, the foreground is given more attention, which highlights the target object and effectively suppresses the noise and background. Preliminary experiments are conducted on an attention-based object detector using the DOTA dataset to test the capability of attention mechanism in extracting the contextual information of objects, especially in complex environments.