Enhanced Thermal Human Detection in Military Applications Using Deep Learning

Abstract

For monitoring and recognizing objects, thermal detection is crucial, particularly in sectors like the military. Typical imaging techniques employ light; thermal imaging uses heat signatures instead. This makes it possible to see clearly in completely dark, smoke-filled, and low-light conditions. The potential of deep learning, or artificial intelligence, in conjunction with thermal imaging technology, is increased by improving object recognition, tracking, and detection precision. When combined, this makes for a potent weapon that may be used for both offensive and defensive military operations, particularly when there is a possibility of hostage-taking, enemy troops being hidden, and risks being recognized. By utilizing YOLOv8 to enhance person identification in thermal photos, this research seeks to further human detection technology. We aim to create a more robust model for human detection in thermal images by a thorough comparison with conventional traditional human detection algorithms and previous iterations of YOLO. Our research advances security procedures, emergency response systems, and surveillance operations by showcasing the greater mAP achieving 96% mAP, efficiency, and dependability of YOLOv8. It is expected that the results of this research will increase the efficacy of security protocols, facilitate prompt emergency responses, and improve surveillance capabilities in a range of practical applications.