A key feature-enhanced network for remote sensing object detection

Currently, Remote sensing object detection methods based on deep learning have been widely used in military investigation, ocean monitoring, urban planning and post-disaster relief. However, many difficulties, such as complex backgrounds, indistinguishable object appearances, large-scale variations, and non-uniform distributions, limit the accuracy of detectors. To deal with these problems, a key feature-enhanced network (KFENet) is proposed to improve the detection accuracy. Specifically, a multi-scale feature fusion (MSFF) module is given to obtain richer feature expression, which can explore inherent structural information of images at different scales. Next, an effective activation detection head (EAD-Head) is designed to adaptively capture key features required for classification and regression tasks, respectively. Finally, we implement our strategy within the framework of YOLOX. Experiments on DIOR and RSOD datasets show that the proposed algorithm outperforms state of the art methods.