# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work
### Drone-Based Wildlife Detection  
In the field of wildlife detection using drones, UAVs have made wildlife conservation technology more accessible. The benefits of aerial imagery from drones have contributed to wildlife conservation. Drones have automated the process of capturing high-altitude aerial photos of wildlife habitats, greatly simplifying the process of obtaining images and monitoring wildlife using automated techniques such as image recognition. This technology eliminates the necessity for capturing images manually and enables more efficient monitoring of wildlife populations. For example, [PERSON] et al. [67] applied UAV technology to wildlife monitoring in tropical and polar environments and demonstrated through their study that UAV counts of nesting bird colonies were an order of magnitude more accurate than traditional ground counts. [PERSON] et al. [68] proposed a computer vision method for quantifying animals in aerial images, resulting in successful detection and counting. The approach holds promise for effective wildlife conservation management. In a study comparing the efficacy of object- and pixel-based classification methods, [PERSON] et al. [69] established that UAV images were more informative than satellite images in mapping mangrove forests in Malaysian wetlands. This opens the possibility for automating the detection and analysis of wildlife in aerial images using computer vision techniques, hence rendering wildlife conservation studies based on these images more efficient and feasible. [PERSON] et al. [70] employed drones in examining marine animals, which enabled the study of numerous aspects of ecology, behavior, health, and movement patterns of these animals. Overall,UAVs and aerial imagery have significant research value and potential for development in wildlife conservation. Moreover, aerial imagery obtained through the use of drones is useful in various wildlife conservation applications due to its high level of accuracy, vast coverage, and broad field of view. This type of imagery, particularly those with 4K resolution or higher, can accurately identify various animal details, such as their category and quantity [71]. For instance, the distinctive stripes of zebras can easily be recognized [72], which is essential in counting the number of animals and assessing their level of endangerment. Aerial photos that cover a broad territory can be useful to survey wildlife in remote or hard-to-reach locations, aiding in complete assessments of animal distribution and habitat utilization [73]. For the advantage of comprehensive coverage, UAVs can survey areas encompassing vast wetlands or dense forests to detect elusive wildlife populations and monitor their ranges. This helps develop effective conservation plans [74]. However, current algorithms for wildlife detection using UAVs still encounter various challenges. Firstly, the accuracy of detecting small targets is inadequate. Existing algorithms fail to satisfy the requirements for accurate detection of small targets. As UAVs capture images from far away, small targets are often only a few tens of pixels. For instance, an enhanced YOLOv4 model was proposed by [PERSON] et al. [75] for detecting UAV aerial images, which achieved only 45% of the mean average precision. [PERSON] et al. [16] discovered that identifying small animals within obstructed jungle environments during UAV wildlife investigations is challenging. Furthermore, meeting practical use standards requires enhanced real-time algorithm performance, which current techniques struggle to achieve the necessary response speed for flight. For instance, [PERSON] et al. [76] utilized the Faster R-CNN method in detecting vehicles in aerial images. The experimental results revealed that the detection speed hit 1.39 s. However, this could not satisfy the real-time requirements. Fortunately, YOLO, a one-stage detection algorithm, exhibits outstanding real-time performance (as mentioned in Section 2.2.1), making it a crucial choice as the primary model for this study.