ArticleSensors (Basel, Switzerland)2025
Threat Assessment of Buried Objects Using Single-Frequency Microwave Measurements.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a lightweight neural network model integrated with a microwave-based detection system for identifying buried objects. The proposed model is trained and tested exclusively on real-world measurements, enhancing its practical relevance and robustness. The system utilizes 16 × 16 scattering parameter (S-parameter) measurements, transformed into a compact 256-dimensional feature vector that captures the microwave response of subsurface materials. This representation enables a neural network architecture with reduced computational complexity while maintaining high accuracy. Experimental evaluations demonstrate that the proposed model achieves an accuracy of 99.83%, an F1 score of 0.989, and a recall of 0.979 in distinguishing hazardous from non-hazardous (safe) objects, outperforming baseline CNN, DRN, and EfficientNet architectures. These results confirm the suitability of the approach in defense and security applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.