Object Relationship Graph Reasoning for Object Detection of Remote Sensing Images
Li Zeng, Yifan Liu, Jingdong Liu, Ye Yuan, Asif Raza, Hong Huo, Tao Fang · 2021
In recent years, deep learning has been successfully applied in object detection of remote sensing images, due to its powerful feature extraction and representation capabilities. However, it usually ignores the relationships among different objects, which greatly hinders the further improvement of the detection precision. In this paper, a novel object relationship graph reasoning deep learning method is proposed for object detection of remote sensing images. The reasoning is carried out on a graph constructed by region proposals. It mainly contains three modules for relationship generation in consideration of different factors that related to object detection: 1) Location-based relationship generation module (LRGM) aims to find spatial relationships among objects according to their positions; 2) Feature-based relationship generation module (FRGM) focuses on mining semantic relationships among objects according to their appearances that implied in feature maps and 3) Class-dependent relationship generation module (CRGM) introduces class dependencies into the relationship generation process. Class dependencies are the prior knowledge that is obtained by computing class co-occurrence. The experimental results on a public twenty-class remote sensing object detection dataset have shown that the proposed method has better detection performance compared to other object detection deep networks with no considering object relationships.