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用于X射线安全检查的轻量级检测方法

Zanshi Wang¹, Xiaohua Wang¹, Yueting Shi²

¹School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

这项研究增强了YOLOv8的X射线安全检查,通过结合适应空间特征融合和协调注意力来提高禁止物品检测的准确性. 修改后的模型显示,在各种测试组中,平均平均精度显著提高.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
安全技术安全技术

背景情况:

由于特征提取不足,样本不平衡和遮,X射线安全检查在检测禁止物品方面面临挑战.
现有的物体检测方法在复杂的安全成像场景中难以准确.

研究的目的:

为了提高在X射线安全检查图像中检测禁止物品的准确性.
开发基于YOLOv8的增强物体检测模型,以解决特征提取,样本失衡和遮蔽方面的局限性.

主要方法:

基于YOLOv8的物体检测方法被提出,集成自适应空间特征融合 (ASFF) 和加权特征连接,以增强尺度特征提取.
嵌入了协调注意模块 (CoordAtt),以改善相关特征的学习.
引入了滑动损失函数来平衡简单和困难的样本,并使用软非最大抑制 (Soft-NMS) 来处理闭塞.

主要成果:

拟议的YOLOv8n模型实现了平均平均精度 (mAP) 的90.2% (易),90.5% (难),79.1% (隐藏),和91.4% (SIXray).
与原始模型相比,mAP在各自的数据集上分别增加了2.7%,3.1%,9.3%和2.4%.
修改后的YOLOv8n模型保持了约300万的低参数数量.

结论:

建议对YOLOv8的改进显著提高了X射线图像中禁止物品的检测准确度.
集成ASFF,CoordAtt,滑动损失和软NMS有效地解决了诸如特征提取,样本不平衡和封闭等挑战.
该模型为先进的安全检查系统提供了计算效率高的解决方案.

关键词:

X射线安全检查检查X射线安全检查这就是YOLOv8的意义.深度学习是一种深度学习.轻量级的模型轻量级的模型.对象检测检测对象检测对象检测

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