De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors.

排雷行动对于受冲突影响地区的人道主义努力和安全至关重要。在本文中，我们解决了提高地雷探测系统的准确性和效率的挑战。我们提出了一种专为基于脉冲感应的金属地雷探测器 (MMD) 量身定制的创新深度学习架构，即所谓的 DL-MMD。我们的方法利用深度神经网络来区分九种不同的材料，验证准确度高达 93.5%。这种高精度使我们不仅能够在没有金属板的情况下区分杀伤人员地雷，而且能够在矿化土壤和非矿化环境中检测微小的 0.2 克垂直纸针。此外，通过比较分析，我们证明与传统的 K 最近邻分类器和支持向量机分类器相比，准确率性能分别提高了 3% 和 7%（大约）。深度神经网络与基于脉冲感应的 MMD 的融合不仅提供了一种经济高效的解决方案，而且还显着加快了排雷行动的决策过程，最终有助于提高这些关键工作的安全性和有效性。