AODet: Anti-Occlusion for Enhanced Small Object Detection in Drone-Based RGBT Imagery

Ziming Gui, Yan Zhang, Xu Lei, Ruixiang Zhang, Wen Yang

Abstract: Drone-based RGBT person detection promotes various applications such as search and rescue due to its maneuverability. While existing research predominantly concentrates on refining fusion strategies and bolstering learning mechanisms for small objects, the pervasive yet unique occlusion challenge in drone-based RGBT settings remains inadequately addressed. In this work, we address the unique challenge of occlusion in the context of RGBT small object detection, particularly emphasizing its vulnerability and the distinct characteristics it exhibits across different modalities. We propose AODet, a novel Anti-Occlusion Detector meticulously crafted to tackle the challenges posed by occlusion in drone-based RGBT object detection. Our proposed approach significantly improves the detection performance of RGBT small objects, surpassing strong baselines on two large-scale datasets, VTUAV-det and RGBTDronePerson, by 1.30 points and 2.24 points in mAPs and ${\text{mAP}}_{50}^{{\text{tiny}}}$, respectively.

External IDs:dblp:conf/igarss/GuiZLZY24