Remote Sensing Object Detection Based on Gated Context-Aware Module

Recently, deep learning algorithms, especially feature pyramid network (FPN), have achieved significant progress in object detection of natural scene images. However, due to the complex scenes of remote sensing images and the diversity of remote sensing objects, FPN still faces the following drawback when applied to remote sensing object detection (RSOD). Specifically, in the original FPN, the features of each proposal are extracted by RoIAlign. However, these features have limited effective receptive fields, making FPN lack crucial contextual information to accurately classify and locate objects, as well as filter some background noises that possess similar appearance with objects. To alleviate the above problem, in this letter, we propose a gated context-aware module (G-CAM) and replace the original RoIAlign in FPN with the proposed G-CAM to adaptively incorporate the useful local context surrounding each proposal and the global context of the whole image into FPN, enabling FPN to effectively detect objects in remote sensing images. Extensive experiments have been conducted on the object detection in optical remote sensing images (DIOR) and RSOD datasets, which validates that the proposed method achieves superior performance to the considered state-of-the-art methods in terms of detection accuracy.