Learning RoI Transformer for Oriented Object Detection in Aerial Images

Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.

1. Introduction

Object detection in aerial images aims at locating objects of interest (e.g., vehicles, airplanes) on the ground and identifying their categories. With more and more aerial images being available, object detection in aerial images has been a specific but active topic in computer vision [3], [29], [36], [6]. However, unlike natural images that are often taken from horizontal perspectives, aerial images are typically taken from bird’s-eye view, which implies that objects in aerial images are always arbitrary oriented. Moreover, the highly complex backgrounds and variant appearances of objects further increase the difficulty of object detection in aerial images. These problems have been often approached by an oriented and densely packed object detection task [37], [31], [12], which is new while well-grounded and have attracted much attention in the past decade [27], [30], [26], [18], [1].