Rotating Objects Detection in Aerial Images via Attention Denoising and Angle Loss Refining

Abstract

With the rapid development of deep learning technology, the problem of detecting objects has been widely studied. A lot of works have made tremendous success on both detection speed and accuracy. However, when detecting small objects, which have less pixels and are more susceptible to noise, the accuracy declines significantly, and this problem is rather serious in Aerial images because most Aerial images contain a lot of small and clustered targets. In this paper, we propose a novel two-stage detector which can not only improve the detection accuracy on small objects but also predict the oriented bounding boxes of them rather than the horizontal ones. Our detection framework can be described as follow: (1) firstly, a basic feature pyramid network is used to generate multi-level feature maps of input images; (2) secondly, Instead of manually designing anchors, we propose a Multi-scale K-means anchor Generator (MKG) to dynamically generate anchors for feature pyramid, which can solve the problem of imbalance distribution of positive anchors; (3) finally, an Attention Denoise Predictor (ADP) and Refine Angle Loss (RAL) are induced to precisely make the rotating bounding boxes predictions. Experiments on the remote sensing public dataset DOTA show the state-of-art performance of our detection framework.