# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 3 Dataset  
Drone footage detection of wildlife in suburban and protected environments is a challenging task due to multiple factors that need to be taken into consideration during the development of our dataset. Conservationists advised that a minimum of 30 to 40 m altitude is the appropriate safety measure for the animals to feel safe in their habitats without disturbing the wildlife during the nesting cycle [26]. Building a reliable detection system in drone footage requires a well-developed dataset for the model to be accurate. Figure 8: YOLOv4-tiny model architecture [10]. Figure 7: YOLOv4 model architecture [19]. Due to the problem in question not containing enough data online, the dataset was created using drone footage recorded from suburban and protected environments. In our dataset, we recorded three videos. Two videos were used to build our dataset, which summed up to 10 min of footage. The 3 rd video was used to visually evaluate our model. Each frame extracted from the footage had a one-second interval. The resulting number of images was 769, but with the help of image tiling/zooming, the samples increased to 10,653. During the development of our dataset, we began by retrieving real-world data; this was achieved by recording footage of suburban environments with several bird species at certain altitudes. The footage we gathered suffered from high light level conditions, which aggravated the resulting predictions of the model. Applying an enhancement algorithm to balance the light condition of the dataset images increased the visibility of the images. Once the footage was retrieved, the next phase was to convert the data into images and begin labelling using DarkMark. Once labelling was complete, we could apply various transformations to our dataset to increase the number of samples, as well as the resulting average precision of our model. Image tiling was one of the more-important techniques that was applied to our image dataset to increase the performance of our model. Image tiling largely affected the outcome of our results, dramatically increasing our model's overall precision, especially because of the task at hand containing small object detections. The challenge of this computer vision task was the scale of the objects we attempted to detect. Some objects within the footage might appear to be too small for the model to detect. Several techniques and steps needed to be applied to the dataset in order to effectively detect small objects within the dataset at an estimated altitude of 30 to 40 m. Based on the altitude of the drone and the distance between each bird, we classified clusters of birds as small-flock or flock this reduced the complexity of the problem in certain scenarios where there was a very large cluster of birds and the altitude was very high. This method is likely to reduce the overall evaluated performance of the model including the average Intersection over Union (IoU) [27]. In order to develop a large and well-structured dataset, techniques such as image enhancement [15], image tiling/cropping/zooming [28], and automatic image labelling using DarkMark were used. We applied gamma correction to each piece of footage to reduce the degradation caused by the sunlight within the footage, and an example of this was presented in Figure 5.  
Another technique that was originally not introduced into our dataset would be to include flip data augmentation. This would better assist the model to minimize any margin of error of any possible missing detections. This parameter works best if the objects are mirrored on both sides, left and right. As mentioned in earlier sections, we retrieved data in suburban environments located at Koronisia, Nisida Kamakia (GPS Pin), and Balla (GPS Pin) in the wetlands of Amvrakikos in the Logarou and Tsoukaliou Lagoons.