Salience Biased Loss for Object Detection in Aerial Images
This addresses the challenging problem of object detection in aerial images for remote sensing applications, representing an incremental improvement over existing methods.
The paper tackles object detection in aerial images by proposing a novel loss function, Salience Biased Loss (SBL), which uses salience information to improve performance; experimental results on the DOTA dataset show SBL-RetinaNet outperforms state-of-the-art models by at least 4.31 mAP and RetinaNet by 2.26 mAP with the same inference speed.
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.