DSENet: An Object-Wise Density-Informed Coarse-to-Fine Object Detector for Aerial Image

Object detection in aerial images remains formidable due to substantial object scale variations, and uneven object distributions. Previous methods widely adopt the coarse-to-fine methodology where detectors focus on large-scale objects coarsely. Sub-regions that contain densely distributed small ones are captured and detected finely. However, two pivotal assessment factors of sub-regions, positional precision, and detection difficulty, deserve further consideration. In this paper, we propose an object-wise density-informed DSENet including consecutive stages termed "Discernment, Selection, Elevation ". Specifically, the sophisticated object-wise density map that considers both object scales and angles, helps discern more positional-precise sub-regions. Then sub-regions with high detection difficulty are selected based on density intensities and coarse detections collaboratively. Finally, the fine detector head instead of the full detector, fine-tuned with selected sub-regions efficiently, elevates what and where coarse detections are mediocre. Extensive experiments show that DSENet achieves state-of-the-art performance on two popular aerial image datasets, VisDrone and DOTA-V1.5.