GODANet: an object detection model for remote sensing images fusing contextual information and dynamic convolution

Abstract

Remote sensing images are characterized by complex feature backgrounds and large target scale differences, so object detection for remote sensing images is a challenging problem. This work proposes a one-stage structure remote sensing image object detection model called GODANet. First, the GODANet incorporates a Global Context Network (GCNet) in the feature extraction structure. The GCNet focuses the model on the image region of interest from a global perspective. Second, the output layer utilizes an omni-dimensional dynamic convolution technique, allowing for more flexible adaptation to targets or edges in specific regions. Finally, an adaptive spatial feature fusion structure, IR-ASFF, which fuses improved-RFB (IRFB) modules is proposed to fuse the critical information of multiple levels of features to realize the adaptability to object detection at different scales. The GODANet efficiently aggregates network performance and possesses two main advantages: adaptability to multi-scale targets and focus on features of interest. The mean average precision (mAP) on the DIOR dataset and the NWPU VHR-10 dataset reached 93.7% and 92.9%, respectively, and compared with YOLOv7, the mAP was improved by 3.1% and 0.3%, respectively. Therefore, we believe the GODANet suits remote sensing image detection tasks.