Density-aware Object Detection in Aerial Images

Abstract

Detecting densely arranged objects is challenging due to the lack of generic definitions and the feature coupling between nearby objects. This paper proposes mathematical definitions of the instance-level, image-level, and dataset-level object density by information theory, called Density Index (DI). The DI shows a high consistency with human perception, serving as a powerful guide for aerial object detection, including data assessment and detector customization. Under the guidance of the DI, we design a DeDet to enhance the detector's performance in detecting densely arranged objects. DeDet pursues accurate location for densely arranged objects by the Density-aware Label Assignment (DLA) and Density-aware Feature Extraction (DFE), conquering the heuristic that the sample assignment and feature extraction are performed independently for each object. Experiments on the DOTA-v1.0 and DOTA-v2.0 show that DeDet can bring a significant improvement to the baseline detector.