用蒙特卡洛丢弃法量化地雷识别不确定性，提升安全决策可靠性。
Uncertainty Quantification In Surface Landmines and UXO Classification Using MC Dropout
- 在微调的ResNet-50中引入蒙特卡洛丢弃，计算预测置信度
- 在干净、噪声和对抗样本上均能准确识别不可靠预测
- 适合关注模型可信度的人道主义排雷应用
利用深度学习检测地表地雷和未爆弹（UXO）在人道主义排雷中展现出前景。然而，确定性神经网络在噪声环境和对抗攻击下易出错，导致漏检或误判。本研究将蒙特卡洛（MC）丢弃方法融入微调的ResNet-50架构，用于地雷与UXO分类，测试数据为模拟数据集。该方法可量化认知不确定性，提供预测可靠性指标，有助于排雷决策。在干净、对抗扰动和噪声测试图像上的实验表明，模型能在复杂条件下识别不可靠预测。该概念验证研究强调了排雷领域中不确定性量化的重要性，揭示了现有神经网络对对抗威胁的脆弱性，并强调开发更鲁棒、可靠模型的必要性。
原文摘要 · Abstract (English)
Detecting surface landmines and unexploded ordnances (UXOs) using deep learning has shown promise in humanitarian demining. However, deterministic neural networks can be vulnerable to noisy conditions and adversarial attacks, leading to missed detection or misclassification. This study introduces the idea of uncertainty quantification through Monte Carlo (MC) Dropout, integrated into a fine-tuned ResNet-50 architecture for surface landmine and UXO classification, which was tested on a simulated dataset. Integrating the MC Dropout approach helps quantify epistemic uncertainty, providing an additional metric for prediction reliability, which could be helpful to make more informed decisions in demining operations. Experimental results on clean, adversarially perturbed, and noisy test images demonstrate the model's ability to flag unreliable predictions under challenging conditions. This proof-of-concept study highlights the need for uncertainty quantification in demining, raises awareness about the vulnerability of existing neural networks in demining to adversarial threats, and emphasizes the importance of developing more robust and reliable models for practical applications.
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