Confusion-Resistant Learning for Few-Shot Oriented Object Detection in Aerial Images

Abstract

This study is devoted to few-shot oriented object detection in aerial images, aiming to enhance detection performance for novel object classes, using only limited supervised samples. Currently, most few-shot object detection models adopt the two-stage fine-tuning approach (TFA), which consists of a base training stage and a few-shot fine-tuning stage. However, the region proposal network (RPN) suffers from foreground–background classification confusion and the rotation angle ambiguity of the square-like bounding boxes. These issues significantly degrade the detection performance for novel categories. To this end, we propose a confusion-resistant learning (CRL) for few-shot aerial oriented object detection. CRL contains a classification reweighting scheme (CRS) and an Edge-Vectors Cosine Similarity (EVCS) Loss. First, the CRS utilizes credible bounding box regression outputs from the base training stage to assist foreground–background classification learning. This process suppresses classification confusion and improves accuracy for novel categories. Second, we propose an EVCS Loss, which builds upon the Kalman filtering IoU (KFIoU) loss. The EVCS Loss alleviates rotation angle confusion for square-like boxes by maximizing the cosine similarity between the edges of the ground-truth and predicted bounding boxes. In addition, CRL can be plugged into the existing two-stage oriented object detectors. Extensive experiments on DOTA and DIOR-R oriented object detection benchmarks show that, compared with the ReDet-KFIoU baseline, our CRL achieves up to 2.4% overall AP50 improvement and 1.8% novel-class AP50 improvement on DOTA, and yields up to 2.0% overall AP gain and 2.3% novel-class AP gain on DIOR-R, providing direct quantitative evidence for the effectiveness of our method.