数据增强X射线图像用于核物品的自动货物检查
Haneol Jang1, Chansuh Lee2,3, Hansol Ko2
1Department of Computer Engineering, Hanbat National University, Daejeon 34158, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种新的数据增强方法,使用合成X射线图像来改进自动核物体检测. 这种技术通过帮助防止在海关检查期间非法转移核材料来提高安全性.
科学领域:
- 核安全和海关检查技术.
- 人工智能和机器学习在安全方面的应用.
背景情况:
- 有效的管理系统对于防止核物品非法转移至关重要.
- 通过X射线成像自动检测核材料对于海关清关至关重要.
- 获得足够的X射线训练数据用于货物检查模型,特别是核燃料和离心机等物品,是一个重大挑战.
研究的目的:
- 提出和评估一种用于生成合成X射线图像的新型数据增强技术.
- 为了解决核物体检测模型的X射线训练数据的稀缺问题.
- 为了提高货物检查中使用的语义细分模型的性能.
主要方法:
- 开发了一种数据增强方法,将核物品的X射线图像与X射线货物背景图像相结合.
- 生成合成X射线图像,以创建一个更大,更多样化的训练数据集.
- 使用增强数据集训练和评估代表性的语义细分模型.
- 进行了定量和定性绩效评估.
主要成果:
- 拟议的数据增强方法有效地生成用于训练的合成X射线图像.
- 在合成数据中包含多个项目插入和由此导致的隐藏,显著影响了细分模型的性能.
- 经过训练的模型在货物中识别核物品方面表现出更好的能力.
结论:
- 开发的数据增强技术显示了增强自动货物检查系统的前景.
- 这项研究有助于改善在港口和机场非法贩运核物品的预防.
- 需要进一步研究阻塞和多项插入的影响,以优化模型性能.
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