Closing the Sim-to-Real Gap: Enhancing Autonomous Precision Landing of UAVs with Detection-Informed Deep Reinforcement Learning

Crowd Counting with human localization task plays a key role in ensuring public safety during large gatherings events. Most prominent works in this area, use large and computationally demanding deep learning model architectures, which require substantial computational power, limiting their usage in a real-world scenario under resource constraints. To alleviate this problem, we propose an improved version of HR-Net, which is substantially smaller and faster than the original, but preserves its localization and counting performance. Through targeted removal of unnecessary modules and branches, we demonstrate an increase in frames-per-second by 37.71\% on an Nvidia Jetson Orin, and a reduction of GMACs and parameters by 77.41\% and 73.07\% respectively, while retaining competitive localization and counting performance, specifically for aerial imagery scenarios. Our modifications enable the algorithm to process in real-time higher resolution images, which is crucial when dealing with small objects. Furthermore, because most crowd counting datasets contain random images gathered from the web, and limited aerial images of crowds, we introduce a specialized dataset of high-resolution aerial imagery for sparse and dense crowds in various environments, that contains images from already available repositories, and also includes new drone-captured annotated data.