Few-Shot Object Detection for Remote Sensing Imagery Using Segmentation Assistance and Triplet Head

The emergence of few-shot object detection provides a new approach to address the challenge of poor generalization ability due to data scarcity. Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in the realm of remote sensing, this technology is still lagging behind. Furthermore, many established methods rely on two-stage detectors, prioritizing accuracy over speed, which hinders real-time applications. Considering both detection accuracy and speed, in this paper, we propose a simple few-shot object detection method based on the one-stage detector YOLOv5 with transfer learning. First, we propose a Segmentation Assistance (SA) module to guide the network's attention toward foreground targets. This module assists in training and enhances detection accuracy without increasing inference time. Second, we design a novel detection head called the Triplet Head (Tri-Head), which employs a dual distillation mechanism to mitigate the issue of forgetting base-class knowledge. Finally, we optimize the classification loss function to emphasize challenging samples. Evaluations on the NWPUv2 and DIOR datasets showcase the method's superiority.