• Developed a comprehensive dataset of bird images from video frames, including various species, poses, and backgrounds, to serve as a foundation for training and testing the YOLOv4 model.
• Employed and optimized YOLOv4 for bird detection in aerial images using Python and Tensorflow, with a focus on improving model accuracy and robustness.
• Evaluated the performance of YOLOv4, comparing results with YOLOv3 and showed improved detection performance of YOLOv4 in the presence of occlusion and overlapping cases.