Solve Point Competition with Label Reassignment in Unmanned Aerial Vehicle Object Detector

Abstract

Unmanned Aerial Vehicle (UAV) object detection is impeded by the difficulty of accurately identifying small, densely packed targets. Despite the computational and real-time constraints of UAV platforms, point-based detection methods are favored for their efficiency. However, these methods encounter issues with point competitions due to the dense distribution of targets, resulting in low precision and recall of UAV datasets. This study proposes label reassignment (LR) to mitigate the competitions arising from the label assignment process, focusing on intra-group competitions (IGC) and invasion competitions (IVC). By introducing extended points, our approach enhances accuracy of detectors. Label reassignment also overcomes the secondary competitions (SC) after introducing the extended points. Experimental results demonstrate the effectiveness of our strategy in reducing competitions and improving model accuracy.