Multi-Sensor Fusion for Remote Sensing of Metallic and Non-Metallic Object Classification in Complex Soil Environments and at Different Depths

The global metal detector (MD) market is predicted to grow to U.S.
${\$}$
2.74 billion by 2030, with a compound annual growth rate (CAGR) of 7.66%, indicating potential expansion in the industry. Metallic and non-metallic material/object detection is pivotal to various technical applications and paramount in ensuring structural integrity, operational efficiency, and overall safety. This study analyzes a multi-sensor fusion approach for classification of metallic and non-metallic objects in complex soil environments at various depths. The adopted approach is beneficial in scenarios where single-sensor systems fail to provide adequate performance. We investigated the integration of magnetic induction spectroscopy (MIS) and ground penetrating radar (GPR) sensors for binary, soil, depth, and multiclass classification. A dataset comprising 14 objects was trained and tested on six AI models (900 variants). The best-performing 384 model variants, which have the highest accuracy, precision, and recall, reaching 98%+ and with minimal Type I and II errors, are reported here. The best accuracy for each classification problem (binary, soil type, depth, and multiobject) corresponding to each set of input data (MIS, GPR, and MIS+GPR) has been summarized and reported. The analysis shows that different AI models have performed better in different scenarios. The study’s implications extend to defense, archeology, and geology, offering enhanced object detection in complex soil environments. These findings underscore the multi-sensor fusion approach’s significance for advancements in remote sensing and classification technologies.