Global to Local: A Scale-Aware Network for Remote Sensing Object Detection

With the wide application of remote sensing images (RSIs) in military and civil fields, remote sensing object detection (RSOD) has gradually become a hot research direction. However, we observe two main challenges for RSOD, namely, the complicated background and the small objects issues. Given the different appearances of generic objects and remote sensing objects, the detection algorithms designed for the former usually cannot perform well for the latter. We propose a novel global-to-local scale-aware detection network (GLSANet) for RSOD, aiming to solve the abovementioned two challenges. First, we design a global semantic information interaction module (GSIIM) to excavate and reinforce the high-level semantic information in the deep feature map, which alleviates the obstacles of complex background on foreground objects. Second, we optimize the feature pyramid network to improve the performance of multiscale object detection in RSIs. Finally, a local attention pyramid (LAP) is introduced to highlight the feature representation of small objects gradually while suppressing the background and noise in the shallower feature maps. Extensive experiments on three public datasets demonstrate that the proposed method achieves superior performance compared with the state-of-the-art detectors, especially on small object detection datasets. Specifically, our algorithm reaches 94.57% mean average precision (mAP) on the NWPU VHR-10 dataset, 95.93% mAP on the RSOD dataset, and 77.9% mAP on the DIOR dataset.