Hierarchical Information Enhancing Detector for Remotely Sensed Object Detection

For the remote sensing object detection task, two-stage networks are widely used due to their high accuracy. These networks roughly predict the proposal regions containing potential objects. It is assumed in these methods that the sizes of these regions are close to that of the corresponding real object. However, this assumption is not always true. Consequently, the detector is affected by the size-unfitting proposal regions. In this letter, a hierarchical information enhancing detector (HIE-Det) is advocated to deal with this issue. First, the important semantic reinjection (ISR) module is proposed to mitigate the lack of object semantics caused by the size-unfitting problem. Compared with the normal detectors, the ISR module increases the proportion of information on objects and improves the effectiveness of the detection model. Second, the object boundary enhancing (OBE) module is proposed to improve the robustness of the regression. The OBE module introduces the convolutional branch stacking multigranularity grids for the same proposal region. Multiple granularity levels improve the robustness of the model to the different degrees of proposal size unfitting. Finally, to evaluate the effectiveness of the HIE-Det on multiscale datasets in a balanced and effective manner, we propose the scale-modulating scores (S-scores), i.e., scale-modulating average precision (sAP) and scale-modulating average recall (sAR). Compared with the other comprehensive scores, the S-scores are rid of the sample amounts and give priority to weaker indices. Implementing the proposed HIE-Det, S-scores {sAP, sAR} are, respectively, improved from {17.3%, 29.7%} to {34.8%, 43.2%}, reaching the state-of-the-art performance on the HRRSD dataset. These experiments verify the effectiveness of the proposed HIE-Det.