IoT-Integrated Reinforcement Learning-Based Mine Detection System for Military and Humanitarian Applications
Year: 2025
Type: article
Abstract: This research proposes an advanced system for landmine detection combining the internet of things and reinforcement learning, which seeks to resolve issues in conventional methods that suffer from high false positive rates, slow reaction times, and poor adaptability across different environmental conditions. Others like metallic detectors and sniffer dogs also pose greater danger for wrong threat identification, more so due to sluggish attempts. The system proposed in this study is novel in that it customizes metal detection by integrating a sensor into military boots, thus permitting constant scanning without the use of hands. A meta-learning-based Machine Learning model improves detection accuracy. It was found that reward-driven reinforcement learning regulation improves mine detection accuracy, increases the analysis attempts in each evaluation phase, and alters the strategic settings. The range of analysis conducted during this study validates the argument in question but this reworking of the system does not fully resolve the issues. The innovation is having that with proper situational awareness this model enables real-time implementation of IoT devices. This adaptable system is not only advantageous for military endeavors but can also be useful for demining activities. More robust multisensory capabilities are essential to facilitate effective and safe landmine inspection all over the globe, so follow-up studies should concentrate on field trials with accompanying iterative improvements.
Authors: Subir Gupta, Upasana Adhikari, Dipankar Roy, Sudipta Hazra
Institution: Haldia Institute of Technology
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412910500