Deep Learning-Based Aerial Object Detection for Unmanned Aerial Vehicles

The proliferation of drones and unmanned aerial vehicles (UAVs) across various sectors, including civil, military, and business applications, has underscored the need for effective collision prevention measures and enhanced surveillance capabilities. Military deployments and civilian applications increasingly rely on UAVs for swift reconnaissance and various tasks. However, the surge in UAV numbers has heightened collision risks, necessitating robust collision prevention measures. In the context of the research's questions and purposes, this research aims to enhance UAV capabilities for efficient and dependable operations in dynamic environments. Specifically, it seeks to improve collision avoidance and surveillance through Charge-Coupled Device (CCD) sensors and deep learning techniques. The research leverages deep learning architectures, including You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (R-CNN), and EfficientDet, in conjunction with CCD sensors for object detection. It conducts a comprehensive comparative analysis to evaluate the performance of these architectures, with a particular focus on YOLOv5, using the UAVDT dataset. The comparative analysis reveals that YOLOv5 outperforms other architectures in terms of accuracy and speed for aerial object detection, especially when applied to the UAVDT dataset. YOLOv5 demonstrates remarkable realtime object detection capabilities, including the identification of diverse objects. While Faster R-CNN and EfficientDet models offer competitive accuracy, they require longer inference times and more training epochs to achieve comparable results. This study showcases the potential of deep learning algorithms to enhance UAV capabilities, making them more efficient and reliable in dynamic environments, thus contributing to the advancement of UAV technology. The research findings presented herein have significant implications for the ongoing development and deployment of UAVs across various sectors.