标题： RestoreAI -- 基于模式的剩余爆炸物风险评估 显示英文标题

标题： RestoreAI -- Pattern-based Risk Estimation Of Remaining Explosives

摘要： 地雷清除是一个缓慢且资源密集的过程，影响着超过60个国家。虽然人工智能已被提出用于增强爆炸物（EO）的检测，但现有方法主要集中在物体识别上，对基于空间模式信息的地雷风险预测关注较少。本研究旨在回答以下研究问题：如何利用人工智能从地雷模式中预测地雷风险以提高清除效率？为此，我们引入了RestoreAI，一个基于模式的风险评估人工智能系统。RestoreAI是第一个利用地雷模式进行风险预测的人工智能系统，提高了在土地释放前估计遗漏爆炸物剩余风险的准确性。我们特别关注RestoreAI三个实例的实现，分别是线性、曲线和贝叶斯模式排雷器。首先，线性模式排雷器使用主成分分析（PCA）中的线性地雷模式进行地雷风险预测。其次，曲线模式排雷器使用主曲线中的曲线地雷模式。最后，贝叶斯模式排雷器通过使用贝叶斯模式风险预测来结合先验专家知识。在真实世界地雷数据上的评估显示，RestoreAI显著提高了清除效率。表现最好的基于模式的排雷器在每个时间步平均清除地雷的比例提高了14.37个百分点，并且比最佳基线排雷器定位所有地雷所需的时间减少了24.45%。有趣的是，线性和曲线模式排雷器的表现没有显著差异，这表明更高效的线性模式是风险预测的一个可行选项。 显示英文摘要

摘要： Landmine removal is a slow, resource-intensive process affecting over 60 countries. While AI has been proposed to enhance explosive ordnance (EO) detection, existing methods primarily focus on object recognition, with limited attention to prediction of landmine risk based on spatial pattern information. This work aims to answer the following research question: How can AI be used to predict landmine risk from landmine patterns to improve clearance time efficiency? To that effect, we introduce RestoreAI, an AI system for pattern-based risk estimation of remaining explosives. RestoreAI is the first AI system that leverages landmine patterns for risk prediction, improving the accuracy of estimating the residual risk of missing EO prior to land release. We particularly focus on the implementation of three instances of RestoreAI, respectively, linear, curved and Bayesian pattern deminers. First, the linear pattern deminer uses linear landmine patterns from a principal component analysis (PCA) for the landmine risk prediction. Second, the curved pattern deminer uses curved landmine patterns from principal curves. Finally, the Bayesian pattern deminer incorporates prior expert knowledge by using a Bayesian pattern risk prediction. Evaluated on real-world landmine data, RestoreAI significantly boosts clearance efficiency. The top-performing pattern-based deminers achieved a 14.37 percentage point increase in the average share of cleared landmines per timestep and required 24.45% less time than the best baseline deminer to locate all landmines. Interestingly, linear and curved pattern deminers showed no significant performance difference, suggesting that more efficient linear patterns are a viable option for risk prediction.