# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 2 Materials and Methods
### Image Processing  
Image processing is a multidisciplinary field that involves the manipulation and analysis of images to extract valuable information or enhance specific features. In the context of this research, image processing serves as a pivotal component in interpreting the visual data captured by drones during surveillance missions. The image processing pipeline typically begins with the acquisition of images, acquired by drones equipped with cameras. These images form the raw data that undergoes a series of processing steps to derive meaningful insights. The first significant phase is preprocessing, where acquired images undergo cleaning and enhancement procedures. Operations such as noise reduction, contrast adjustment, and normalization are employed to refine the quality of the images, ensuring a consistent and reliable dataset for subsequent analysis. Following preprocessing, image segmentation becomes a crucial step. This process involves dividing the image into meaningful regions or objects [20]. Segmentation is particularly valuable for isolating areas of interest within the surveillance context, aiding in the identification of distinct objects or regions within the aerial imagery. Once segmentation is complete, the focus shifts to feature extraction. Features represent distinctive characteristics inherent in objects or regions within the image [21]. These features can span various attributes, including color, texture, shape, and spatial relationships. Extracting relevant features is essential for subsequent analysis and the identification of specific elements within the surveillance area. The culmination of these image processing steps contributes to a refined and analytically rich dataset. Image recognition, a subset of computer vision, focuses on enabling machines to comprehend and interpret the content within images. The fundamental goal is to develop algorithms and models that possess the ability to identify and categorize objects, scenes, or patterns present in visual data. The process involves teaching a machine to recognize specific features or characteristics in images and associating them with predefined classes or categories. This recognition capability empowers machines to autonomously classify images and make informed decisions based on their content. The image recognition workflow consists of several key components. It commences with data collection, where a diverse and representative dataset of images is gathered, covering the various classes the model is intended to recognize. Subsequently, preprocessing techniques are applied to clean and enhance the acquired images, ensuring consistency and improving overall quality [22]. Feature extraction follows, where relevant characteristics within the images are identified, such as color, texture, and shapes, forming the basis for classification. The training phase involves the utilization of machine learning algorithms, and convolutional neural networks (CNNs), to learn the relationships between the extracted features and the corresponding image classes [23]. Testing and evaluation assesses the model's performance on new, unseen images, gauging its ability to generalize. Finally, the trained model is deployed in real-world applications to automatically analyze and classify images. Image processing is an essential precursor to image recognition, as it encompasses a set of techniques designed to manipulate and enhance images. During preprocessing, image processing techniques are employed to ready the images for analysis, including tasks such as normalization, contrast adjustment, and noise reduction. Furthermore, image processing plays a pivotal role in feature extraction, aiding in the identification and enhancement of relevant aspectswithin images [24]. The synergy between these fields ensures that the images used for training and deploying image recognition models are optimized, standardized, and enriched, contributing to the overall accuracy and effectiveness of the recognition process.