# A Few-Shot Object Detection Method for Endangered Species
## 1 Introduction  
The crisis in biodiversity and the destruction of ecosystems continues to accelerate, leading to the extinction of many species and the collapse of ecosystems on a global scale [1]. Endangered species play an important role in maintaining ecological balance and are key in many scientific research fields, such as bionics, medicine, and pharmacology [2], so the detection and research of endangered animals are of great significance. Deep learning models have made significant progress, from the initial convolutional neural networks to today's hierarchical and complex network structures, such as SSD [3], YOLO [4], RCNN [5], etc. [PERSON] [6] utilized AlexNet [7] and Inception v3 [8] to assess the KTH dataset, revealing the superior performance of deep learning algorithms over machine learning ones in animal species recognition. [PERSON] [9] introduced a transfer deep learning approach employing super-resolution Mask RCNN [10] for bird recognition, enhancing input image resolution using super-resolution technology. [PERSON] [11] proposed a transfer learning strategy for training neural models, leveraging pre-trained Mask RCNN to extract bird ROIs from photos, followed by fine-tuning through transfer learning methods using datasets. WilDect-YOLO [12] significantly enhances endangered species detection accuracy by incorporating a residual block into YOLOv4's [13] CSP-Darknet53 [14] backbone, alongside implementing spatial pyramid pooling and an improved path aggregation network. However, a common feature of these networks is their more significant reliance on large-scale data. Considering the limited number of endangered animals and the relative difficulty in obtaining their image data, while traditional object detection networks require large-scale data support, applications in endangered animal scenarios face multifaceted challenges. In contrast, few-shot object detection (FSOD) [15; 16; 17] provides a solution designed to be able to quickly detect new objects from a very small number of annotated samples of new classes. FSOD is generally divided into two phases. Firstly, training on a base class dataset with rich annotation information is performed to build a base class detection model. Subsequently, the task of detecting new classes is accomplished with the minimally annotated new class dataset and the prior knowledge provided by the base class model. FSRW [18] is a lightweight meta-model based on YOLOv2 [19] that applies a reweighting module to emphasize the importance of the class prototype vectors and uses meta-features to facilitate the detection of new classes of objects. Fan [20] enhances information interaction by introducing attention to the Region Proposal Network (RPN), and multi-relationship modules are designed to facilitate the detection of new classes of objects by introducing the Attention-Region Proposal Network and multi-relational modules to enhance information interaction. Recent studies have shown that some fine-tuning-based few-shot object detection methods outperform meta-learning-based methods. TFA [21] proposes a simple transfer learning-based method to fine-tune only the last two fully connected layers of the detector to detect new objects. FSCE [22] proposes a few-shot object detection based on TFA by contrast proposal coding, which enhances the main region of interest (RoI) header by using a contrast branch. FSOD has demonstrated good detection performance and generalization ability on generalized datasets, such as Pascal VOC [23] and MS COCO [24]. However, in the case of endangered species with diverse and complex species, the highly similar feature details among some categories lead to misclassification problems in FSOD.