Evaluating YOLOv4 and YOLOv5 for enhanced object detection in UAV-based surveillance

Abstract

Traditional surveillance systems often rely on fixed cameras with limited coverage and human monitoring, which can lead to potential errors and delays. Unmanned Aerial Vehicles (UAVs) equipped with object detection algorithms, such as You Only Look Once (YOLO), offer a robust solution for dynamic surveillance, enabling real-time monitoring over large and inaccessible areas. In this study, we present a comparative analysis of YOLOv4 and YOLOv5 for UAV-based surveillance applications, focusing on two critical metrics: detection speed (Frames Per Second, FPS) and accuracy (Average Precision, AP). Using aerial imagery captured by a UAV, along with 20,288 images from the Microsoft Common Objects in Context (MS COCO) dataset, we evaluate each model’s suitability for deployment in high-demand environments. The results indicate that YOLOv5 outperforms YOLOv4 with a 1.63-fold increase in FPS and a 1.09-fold improvement in AP, suggesting that YOLOv5 is a more efficient option for UAV-based detection. However, to align with recent advancements, this study also highlights potential areas for integrating newer YOLO models and transformer-based architectures in future research to further enhance detection performance and model robustness. This work aims to provide a solid foundation for UAV-based object detection, while acknowledging the need for continuous development to accommodate newer models and evolving detection challenges.