# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 1 Introduction  
Achieving optimal object detection precision is crucial for effective monitoring and reconnaissance using aerial surveillance systems. With the proliferation of unmanned aerial vehicles (UAVs), there is a growing need to enhance the accuracy of object detection methodologies. This article presents a novel approach that integrates Delaunay triangulation with multiple-unmanned aerial vehicle systems, aiming to improve object detection accuracy and overcome limitations of conventional single UAV approaches. The use of UAVs for aerial surveillance has gained significant attention in recent years due to their ability to navigate challenging terrains, provide large-scale coverage, and capture high-resolution imagery. However, the effectiveness of a traditional single UAV-based surveillance system is restricted by their limited capabilities to cover large areas and detect objects with high precision, especially when operating in close proximity [1, 2]. To address these limitations, this research explores the benefits of employing multiple UAVs in collaborative efforts guided by Delaunay triangulation. Delaunay triangulation is a geometric algorithm that divides a set of points in a plane into a set of non-overlapping triangles, such that no point is inside the circumcircle (the circle that passes through all three vertices) of any triangle [3]. This triangulation has several desirable properties and has notable applications in positioning and localization, particularly in the context of spatial optimization and efficient coverage [4]. In this research,Delaunay triangulation is used as a geometric approach that involves positioning multiple UAVs at pre-specified locations, creating a network that maximizes coverage and optimizes the collective surveillance capabilities. This approach allows for the systematic division of the surveillance area into smaller triangles, enhancing the precision of object detection. The current state of research in the field of aerial surveillance indicates a significant interest in enhancing object detection accuracy. Several studies have focused on developing advanced image processing techniques to identify objects accurately and efficiently [5, 6, 7, 8]. Controversial and diverging hypotheses regarding the optimal strategies for aerial surveillance also exist [9, 10]. Some researchers argue that single UAV approaches with specialized sensors and algorithms yield satisfactory results, while others advocate for the scalability and collaboration benefits offered by multiple UAV systems [11, 12]. This research aims to address these diverging viewpoints by investigating the performance of a multiple-UAV system integrated with Delaunay triangulation. The principal aim of this study is to evaluate the impact of the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, on object detection accuracy. Through advanced image processing techniques and experimental assessment, this research seeks to quantify the improvements achieved by this approach compared to traditional single UAV surveillance systems operating at close proximity. In essence, this study aims to answer the question: how does the integration of Delaunay triangulation with multiple-unmanned aerial vehicle systems enhance object detection accuracy in comparison to traditional single UAV approaches? The image processing methodology in this research leverages Amazon Rekognition, a robust and scalable solution for object detection in aerial surveillance [13]. Amazon Rekognition offers a pre-trained deep learning model capable of identifying and locating objects within images, making it suitable for real-time analysis of UAV-captured data. Amazon Rekognition allows the system to automatically identify and locate objects of interest in the surveillance area. This includes vehicles, individuals, and other relevant items depending on the application scenario.