Automatic People Detection Based on RGB and Thermal Imagery for Military Applications

Automatic detection of people in military applications offers numerous benefits that enhance situational awareness, operational efficiency, and overall security. RGB images offer high resolution and color information, making them suitable for detailed visualization, but they are limited by lighting conditions and can struggle to detect people in low-light or nighttime scenarios. Additionally, camouflage and adverse weather can hinder their effectiveness. On the other hand, thermal images detect heat signatures, enabling people detection in darkness and adverse conditions, but they may lack detail, and their cost is higher. Both technologies have their merits, and a combined approach can provide a more comprehensive solution for people detection in various military and security applications. This research contributes to the field of perception systems for military applications, by harnessing the potential of artificial intelligence (AI) and deep learning technologies. This paper presents a comparison of performance of three people detectors based on the YOLOv8 architecture, a computer vision model, namely using 1) RGB images, 2) thermal images, and 3) RGB and thermal images combined. A quantitative analysis of performance of the three models allows to compare their performance in realistic and challenging scenarios. Additionally, a qualitative assessment is conducted to identify specific limitations and advantages associated with each approach, providing valuable insights for further improvement and optimization of these detection systems.