A Multi-Scale Coupled Relationship Activation-Based Few-Shot Detection Method for Endangered Wildlife

Abstract In the domain of monitoring and conservation of rare and endangered wildlife, traditional object detection methods fail to operate effectively due to overfitting caused by limited image data. To address this issue, this paper proposes a small-sample object detection method that utilizes limited support information to achieve accurate detection of endangered wildlife images. Firstly, we introduce the Feature Pyramid Network (FPN) to adapt to foreground targets at various scales. Secondly, we propose a Coupled Relationship Activation (CRA) module that aggregates features from the support set into a comprehensive feature vector and utilizes it to activate similar regions in the query feature. Furthermore, to balance the differences between support and query features, we incorporate cosine similarity and short-circuit structures before and after the activation process, thereby mitigating the impact of activating dissimilar features erroneously. During the above process, we construct a balanced dataset, CRBAD, which comprises images of both endangered and common animal categories, and employ it to conduct practical testing of the proposed method. Experimental results demonstrate that the proposed model achieves state-of-the-art performance in detecting endangered wildlife images.