梳理AI在排爆检测中的研究现状，指出风险预测是薄弱环节。
AI for Explosive Ordnance Detection in Clearance Operations: The State of Research
- 分两类研究：爆炸物目标检测与风险预测，后者更少被关注。
- 现有研究多集中于目标检测，风险预测方法仍不成熟。
- 建议融合多源数据、引入模式化预测，增强模型实用性。
爆炸物排爆作业仍是高度依赖人工的高危任务，技术进步可显著提升效率与安全。近年来，人工智能（AI）在排爆检测领域的研究迅速增长，但涉及领域广泛，难以全面把握发展脉络。本文对相关学术研究进行综述，发现研究主要分为两大方向：爆炸物目标检测与爆炸物风险预测，其中后者远未得到充分重视。基于文献分析，本文提出三项未来研究机遇：一是加强AI在风险预测方面的应用；二是整合不同AI系统与多源数据；三是探索新模式以提升风险预测性能，如基于模式的预测方法。最后，文章展望了未来发展方向，强调传统机器学习（ML）的作用，主张动态融入专家知识，并注重将AI系统有效嵌入实际排爆操作流程。
原文摘要 · Abstract (English)
The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its efficiency and effectiveness. Research on artificial intelligence (AI) for EO detection in clearance operations has grown significantly in recent years. However, this research spans a wide range of fields, making it difficult to gain a comprehensive understanding of current trends and developments. Therefore, this article provides a literature review of academic research on AI for EO detection in clearance operations. It finds that research can be grouped into two main streams: AI for EO object detection and AI for EO risk prediction, with the latter being much less studied than the former. From the literature review, we develop three opportunities for future research. These include a call for renewed efforts in the use of AI for EO risk prediction, the combination of different AI systems and data sources, and novel approaches to improve EO risk prediction performance, such as pattern-based predictions. Finally, we provide a perspective on the future of AI for EO detection in clearance operations. We emphasize the role of traditional machine learning (ML) for this task, the need to dynamically incorporate expert knowledge into the models, and the importance of effectively integrating AI systems with real-world operations.
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