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The rapid proliferation of Unmanned Aerial Vehicles (UAVs) or drones in military, disaster management, business, and entertainment applications has raised concerns about their potential airspace risks. Researchers are increasingly focused on developing methods for detecting and tracking UAVs with various data sources like radar, visual, acoustic, and radio-frequency data available. Among these, visual data stands out as cost-effective and amenable to analysis using Computer Vision (CV) techniques. However, vision-based tasks present challenges such as occlusions, shaky footage, and small UAVs at a distance, requiring timely and computationally efficient detection, especially given limited onboard computational power. To address these challenges, researchers are employing advanced Convolutional Neural Networks and Transformer-based deep learning models alongside classical image processing …