基于使用轻量级模型的X射线图像检测禁止物品的研究
Tianfen Liang1,2, Hao Wen2, Binyu Huang1
1Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种轻量级的深度学习算法,用于在X射线安全查中自动检测禁止物品. 新方法提高了准确性,减少了与疲劳相关的错误,改善了公共安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 安全技术 安全技术
背景情况:
- 对公共安全而言,X射线安全检查至关重要,但由于疲劳和复杂的场景,手动检查容易出现错误.
- 挑战包括重叠的物品,可变的位置,以及需要便携式检测解决方案.
研究的目的:
- 开发一种轻量级的自动检测方法,在X射线图像中检测禁止物品.
- 与传统的手动检查和现有的自动化系统相比,提高检测准确性和效率.
主要方法:
- 一个深度学习算法,利用具有残余结构和注意力机制的新型骨干网络.
- 整合一个扩展的卷积空间金字塔模块和深度可分离的卷积,用于多尺度特征融合.
主要成果:
- 拟议的轻量级方法实现了最高的检测率95.59%.
- 与YOLOv4-tiny基线相比,显示了1.86%的平均精度 (mAP) 改善.
- 在每秒122 (FPS) 运行,表明高效率.
结论:
- 开发的轻量级自动检测方法有效地提高了在X射线安全查中检测禁止物品的准确性.
- 这项技术提供了一个有前途的解决方案,通过减少人为错误和提高检测效率来提高公共安全.
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