Enhancing Multiple Object Detection in UAV Images: A Comparative Study of YOLO, RCNN and SSD Algorithms

Unmanned Aerial Vehicles (UAVs) have emerged as invaluable tools for various applications, ranging from surveillance and reconnaissance to agriculture and disaster management. The utilization of UAVs for data collection, particularly in the form of high-resolution images, has led to significant advancements in computer vision and machine learning. One of the critical challenges in leveraging UAV imagery lies in the efficient detection of multiple objects within these images, a task that is pivotal for applications such as environmental monitoring, infrastructure inspection, and security surveillance. This research paper aims to contribute to the field of multiple object detection in UAV images by conducting a comprehensive comparative analysis of state-of-the-art machine learning algorithms. The focus is on popular object localization models, including You Only Look Once (YOLO), Region-based Convolutional Neural Network (RCNN), and Single Shot Multi box Detector (SSD). The study extends beyond comparing algorithms and explores different underlying networks connected to these algorithms, aiming to understand how network architecture affects object detection performance.