29 Apr 2026

Deep Learning Approaches for Buried Object Detection in Infrared Imagery

Abstract: Artificial intelligence and machine learning techniques are increasingly utilized to detect buried objects in thermal infrared imagery. This task relies heavily on the quality and diversity of the training dataset, requiring datasets that capture variability present in real-world environments. Synthetic imagery offers a means to expose algorithms to a greater range of conditions than is often available in real-world data alone. This study evaluates the performance of three open-source object detection models—Faster Region-Based Convolutional Neural Network (R-CNN), You Only Look Once (YOLOv8), and Single Shot Multibox Detector—trained using real-world, synthetic, and hybrid datasets. Real-world imagery was collected from a single field site, while synthetic data were generated using the Virtual Environmental Simulation for Physics-Based Analysis software suite. Model performance was evaluated using Intersection over Union and confidence scores. Models trained exclusively on synthetic datasets with limited scene diversity, when tested on real-world imagery from the same location, produce high false-positive and false-negative rates. Detection performance im-proved significantly for Faster R-CNN and YOLOv8 when trained using a hybrid dataset combining real-world and synthetic data. Analysis of red-green-blue histograms revealed differences in pixel intensity distributions between real and synthetic imagery, indicating areas for improving synthetic data generation.