Cascade Detector With Feature Fusion For Arbitrary-Oriented Objects In Remote Sensing Images
Liping Hou, Ke Lü, Jian Xue, Hao Li
Abstract
Detection of multi-class rotated objects is a challenging task in optical remote sensing images because of large-scale variations, arbitrary orientations and complex backgrounds, etc. Most of the state-of-the-art object detectors for natural images, that use horizontal bounding boxes, are not suitable for oriented objects in remote sensing images. In this paper, we propose an end-to-end cascade detector that can effectively detect rotated objects in complex remote sensing images. Specifically, a feature fusion block is designed to capture features with more details. Meanwhile, a supervised spatial attention mechanism is adopted to improve performance in detecting objects with complex backgrounds by weakening noise and enhancing object regions. Finally, to obtain more accurate object position, a cascade of multi-step detection subnet is implemented to refine anchors. Experiments using a publicly available remote sensing dataset DOTA show that our object detector achieves superior performance over other state-of-the-art approaches.