  Object detection in remote sensing, especially in aerial images, remains a
challenging problem due to low image resolution, complex backgrounds, and
variation of scale and angles of objects in images. In current implementations,
multi-scale based and angle-based networks have been proposed and generate
promising results with aerial image detection. In this paper, we propose a
novel loss function, called Salience Biased Loss (SBL), for deep neural
networks, which uses salience information of the input image to achieve
improved performance for object detection. Our novel loss function treats
training examples differently based on input complexity in order to avoid the
over-contribution of easy cases in the training process. In our experiments,
RetinaNet was trained with SBL to generate an one-stage detector,
SBL-RetinaNet. SBL-RetinaNet is applied to the largest existing public aerial
image dataset, DOTA. Experimental results show our proposed loss function with
the RetinaNet architecture outperformed other state-of-art object detection
models by at least 4.31 mAP, and RetinaNet by 2.26 mAP with the same inference
speed of RetinaNet.
