Pareto Refocusing for Drone-View Object Detection

Drone-view Object Detection (DOD) is a meaningful but challenging task. It hits a bottleneck due to two main reasons: (1) The high proportion of difficult objects (e.g., small objects, occluded objects, etc.) makes the detection performance unsatisfactory. (2) The unevenly distributed objects make detection inefficient. These two factors also lead to a phenomenon, obeying the Pareto principle, that some challenging regions occupying a low area proportion of the image have a significant impact on the final detection while the vanilla regions occupying the major area have a negligible impact due to the limited room for performance improvement. Motivated by the human visual system that naturally attempts to invest unequal energies in things of hierarchical difficulty for recognizing objects effectively, this paper presents a novel Pareto Refocusing Detection (PRDet) network that distinguishes the challenging regions from the vanilla regions under reverse-attention guidance and refocuses the challenging regions with the assistance of the region-specific context. Specifically, we first propose a Reverse-attention Exploration Module (REM) that excavates the potential position of difficult objects by suppressing the features which are salient to the commonly used detector. Then, we propose a Region-specific Context Learning Module (RCLM) that learns to generate specific contexts for strengthening the understanding of challenging regions. It is noteworthy that the specific context is not shared globally but unique for each challenging region with the exploration of spatial and appearance cues. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET and UAVDT datasets demonstrate that the proposed PRDet can effectively improve the detection performance, especially for those difficult objects, outperforming state-of-the-art detectors. Furthermore, our method also achieves significant performance improvements on the DTU-Drone dataset for power inspection.