arXiv subjects

Chad Melton

Publications and source records attributed to Chad Melton.

3 recordsLinked to original sources

arXiv2026

UAV Thermal Imagery for Inert Ordnance Screening: Multi Campaign Dataset Development,Object Detection, and Practical Recommendations

Unexploded ordnance (UXO) continues to restrict civilian access, agricultural activity, infrastructure recovery, and environmental remediation in contaminated areas around the world. This study created a multi campaign UAV thermal image data set of inert ordnance, developed a labeled image set from collected imagery, tested object detection models, and identified practical considerations for humanitarian mine action and demining applications. Data were collected during four field campaigns in Tennessee under summer and winter conditions using inert mines, munitions, and other ordnance placed in short grass, tall vegetation, gravel, mulch, rock, compost, and compacted surfaces. Thermal imagery was collected under flight altitutes of 33 m and 15 m. The final source inventory contained 5,855 thermal image label pairs, including 918 positive images and 4,937 background images. After retaining all positive images and downsampling background images, the 33 m dataset contained 420 training and 106 validation images, while the 15 m dataset contained 629 training and 157 validation images. YOLOV11l and RT-DETR-R50 algorithms were trained and evaluated to develop an automated candidate detection model. Practical recommendations include collecting thermal and RGB imagery together, incorporating varied surfaces and background only imagery, considering periods following changes in solar exposure, balancing survey coverage against target pixel representation, calibrating models with representative local data, and retaining qualified human review. The intended use is screening and prioritization for follow on technical survey or EOD assessment, and not a standalone clearance.

cs.CV↗

arXiv2025

Centralized Health and Exposomic Resource (C-HER): Analytic and AI-Ready Data for External Exposomic Research

The Centralized Health and Exposomic Resource (C-HER) project has identified, profiled, spatially indexed, and stored over 30 external exposomic datasets. The resulting analytic and AI-ready data (AAIRD) provides a significant opportunity to develop an integrated picture of the exposome for health research. The exposome is a conceptual framework designed to guide the study of the complex environmental and genetic factors that together shape human health. Few composite measures of the exposome exist due to the high dimensionality of exposure data, multimodal data sources, and varying spatiotemporal scales. We develop a data engineering solution that overcomes the challenges of spatio-temporal linkage in this field. We provide examples of how environmental data can be combined to characterize a region, model air pollution, or provide indicators for cancer research. The development of AAIRD will allow future studies to use ML and deep learning methods to generate spatial and contextual exposure data for disease prediction.

stat.AP↗

arXiv2025

Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety

Applications of generative Large Language Models LLMs are rapidly expanding across various domains, promising significant improvements in workflow efficiency and information retrieval. However, their implementation in specialized, high-stakes domains such as hazardous materials transportation is challenging due to accuracy and reliability concerns. This study evaluates the performance of three fine-tuned generative models, ChatGPT, Google's Vertex AI, and ORNL Retrieval Augmented Generation augmented LLaMA 2 and LLaMA in retrieving regulatory information essential for hazardous material transportation compliance in the United States. Utilizing approximately 40 publicly available federal and state regulatory documents, we developed 100 realistic queries relevant to route planning and permitting requirements. Responses were qualitatively rated based on accuracy, detail, and relevance, complemented by quantitative assessments of semantic similarity between model outputs. Results demonstrated that the RAG-augmented LLaMA models significantly outperformed Vertex AI and ChatGPT, providing more detailed and generally accurate information, despite occasional inconsistencies. This research introduces the first known application of RAG in transportation safety, emphasizing the need for domain-specific fine-tuning and rigorous evaluation methodologies to ensure reliability and minimize the risk of inaccuracies in high-stakes environments.

cs.CL↗