# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 1 Introduction
To fill this gap, we introduce a novel and extensive aerial wildlife detection public dataset called WAID (Wildlife Aerial Images from Drone), aimed at aiding researchers in developing drone-based wildlife detection algorithms. In addition, the data of wildlife captured by drones have unique characteristics. As can be seen from Figure 1, the individual animals in aerial images are small and the proportion varies greatly. This arises not only from differences in drone flight altitudes but also relates to inherent variations in object sizes within the same category. Therefore, it is necessary to overcome the small target problem when using drones to detect wildlife [23]. Currently, researchers have invested substantial efforts in enhancing the accuracy of algorithms for detecting these small targets, but few of them focus on wildlife aerial imagery applications [24]. For instance, [PERSON] et al. [25] proposed an approach that combines contextual models and small region proposal generators to enhance the R-CNN algorithm, thereby improving the detection of small objects in images. [PERSON] et al. [26] introduced a scale-transferable detection network that maintains scale consistency between different detection scales through embedded super-resolution layers, enhancing the detection of multi-scale objects in images. However, although the above work has improved the effect of small target detection to a certain extent, challenges remain, including limited real-time performance, high computational demands, information redundancy, and complex network structures. Figure 1: Example of aerial images of zebra migration at different flight heights. On the one hand, to address real-time wildlife detection, researchers often employ one-stage YOLO methods [27]. For example, [PERSON] et al. [28] used the YOLO method to monitor marine animal populations, demonstrating that YOLO can rapidly and accurately identify marine animals within coral reefs. [PERSON] et al. [29] proposed a high-performance YOLO-based automatic detection model for real-time detection of endangered wildlife. Although advanced versions like YOLOv7 [30] have started addressing multi-scale detection issues, they still struggle with the highly dynamic scenes encountered in drone aerial imagery [31]. On the other hand, researchers have also turned their attention to attention mechanisms to address the small target detection problem. These mechanisms guide the model to focus on critical feature regions of the target, leading to more accurate localization of small objects and reduced computational resource consumption [32]. For instance, [PERSON] et al. [33] introduced a pre-detection concept based on attention mechanisms into the model, which restricts the detection area in images based on the characteristics of small object detection. This reduces redundant information and improves the efficiency of small target detection. [PERSON] et al. [34] proposed an attention-fused feature pyramid network to address the issue of potential target loss in deep network structures. Their adaptive attention fusion module enhances the spatial positions and semantic information features of infrared small targets in shallow and deep layers, enhancing the network's ability to represent target features. [PERSON] et al. [35] introduced a lightweight small target detection network with embedded attention mechanisms. This enhances the network's ability to focus on regions of interest in images while maintaining a lightweight network structure, thereby improving the detection of small targets in complex backgrounds. These studies indicate that incorporating attention mechanisms into models is beneficial for effectively fusing features from different levels and enhancing the detection efficiency and capabilities of small targets. Notably, channel attention mechanisms hold advantages in small target detection [36; 37]. Channel attention mechanisms better consider information from different channels, focusing on various dimensions of small targets and effectively capturing their features [38].