Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite-Based Aircraft Detection

Recent advancements in object detection technology have revolutionized real-time remote sensing capabilities through satellite imagery. Close-orbit satellites, unmanned aerial vehicles, and autonomous vehicles have emerged as preferred platforms for object detection, offering operational advantages across various applications. Deep learning algorithms play a pivotal role in object detection. Aircraft detection is particularly critical in civil and military aviation contexts, facilitating efficient traffic routing at airports and enhancing national defense strategies. However, satellite-based remote sensing faces challenges such as image resolution limitations, environmental influences, and complexities in distinguishing small, densely packed aircraft in datasets. To address these challenges, deep learning techniques harness neural networks optimized for parallel processing on GPUs, significantly improving object detection accuracy and efficiency. This study focuses on evaluating the YOLOv5 detection algorithm for ground aircraft detection using satellite-based aerial imaging. YOLOv5’s single-stage architecture enables real-time detection and classification of objects with enhanced accuracy and efficiency, making it ideal for applications requiring rapid analysis of large-scale aerial images. Leveraging advanced deep learning methodologies, this research assesses YOLOv5’s performance in aircraft detection, highlighting its potential in advancing aviation management, surveillance, and security practices.