Enhanced few-shot object detection for remote sensing images based on target characteristics

Few-shot object detection (FSOD) in remote sensing images (RSIs) is a challenging and hot issue due to the characteristics of targets in remote sensing images such as varying sizes, complex backgrounds, target occlusions, and unbalanced target categories. Two-stage fine-tuning approach (TFA) has been shown to be the most competitive approach for few-shot learning. However, previous methods of fine-tuning remote sensing images using single-feature improvements had a limited effect on the result. In this paper, we propose a novel few-shot object detection method that coordinates multiple methods to gradually improve detection accuracy by focusing on the characteristics of remote sensing targets. More specifically, our model contains three main components: a novel context-aware weighted feature fusion module (CA-WFFM) that enhances the discrimination between background and foreground structures in complex scenarios, an edge detection block (EDB), which combines handcrafted features, that enhances the stability of the network when the target is occluded or the sample is limited, and a selective random neighborhood oversampling (SRNO) strategy, that balances the base and novel category samples. These coordinated improvements were evaluated using the Drones In Optics Recognition (DIOR) and NWPU-VHR-10.v2 (VHR-10.v2) datasets with multiple training sample splits. Experimental results indicate that our method surpasses existing methods in novel class detection and also provides a new research approach for few-shot object detection. This involves designing algorithms based on target characteristics to enhance the overall detection capability of few-shot object detection models.