SOON: Specifically Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

With great significance in military and civilian applications, detecting indistinguishable small objects in wide-scale remote sensing images is still a challenging topic. In this work, we propose a specially optimized one-stage network (SOON) focusing on extracting spatial information of high-resolution images by understanding and analyzing the combination of feature and semantic information of small objects, which consists of feature enhancement, multi-scale detection, and feature fusion. The first part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the specific parts of the network where the information of small objects mainly exists. The second part is achieved by four detectors with different sensitivities accessing to the fused and enhanced features, which enables the network to make full use of features in different scales. The third part consolidates the high-level and low-level features by adopting up-sampling, concatenation and convolution operations to build a feature pyramid structure, which explicitly yields strong feature representation and semantic information. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage for densely arranged objects. Note that the split and merge strategy, as well as the multi-scale training strategy, are employed in this work. Extensive experiments and thorough analysis are performed on the NWPU VHR-10-v2 dataset and the ACS dataset as compared with several state-of-the-art methods, in which satisfactory performance verifies the effectiveness of the design and optimization. The code will be released for reproduction.