Correlated NMS: Establishing Correlations Between Dense Predictions of Remote Sensing Images

Object detection is an important task for remote sensing image analysis, which aims to identify and locate objects within captured remote sensing images. Several object detection methods have been proposed, among which Non-maximum suppression (NMS) is an essential ingredient of these methods. Although simple and useful for object detection, the performance of detection methods with NMS will degeneradte when the objects within remote sensing images are dense. One of the main reasons is the presence of severe occlusion in some remote sensing images, which can easily mislead NMS to suppress the nearby candidate box of different object from its central box. To address this problem effectively, we propose to measure the correlation between the candidate box and the central box, from which if these two boxes come from the same object can be estimated. Building on this idea, we propose Correlated NMS, which can adaptively adjust the suppression threshold between the candidate box and its central box based on whether they tend to represent the same object. Experimental results demonstrate the effectiveness of the proposed method.