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ScanGuard-YOLO:增强X射线禁止物品检测,显著提高性能

Xianning Huang¹, Yaping Zhang¹

¹School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.

Sensors (Basel, Switzerland)

|January 11, 2024

概括

增强的YOLOv5网络ScanGuard-YOLO通过解决对象遮蔽和复杂的背景,显著改善了X射线图像中的禁止物品检测. 它提高了回忆和F1分数,提供了更好的安全查能力.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

在X射线禁止物品检测中,回忆率较低的原因是物体封闭和复杂的背景.
现有的方法在安全检查中难以准确识别隐藏的物品.
需要先进的深度学习模型来进行强大的威胁检测.

研究的目的:

开发一个先进的X射线禁止物品检测网络,ScanGuard-YOLO,以提高检测准确度.
提高回忆率和F1分数,在具有挑战性的X射线图像中识别禁止物品.
为了利用YOLOv5架构,使用新的模块来实现卓越的性能.

主要方法:

整合了具有扩展卷积的RFB-s模块,以增强在骨干中的全球特征捕获.
利用子中的RepGFPN模块来融合多尺度信息,以更好地表示对象.
引入了一种新的检测头,将规模,空间和任务意识统一起来,并使用WIOUv3损失来限制框回归.

主要成果:

与基线相比,ScanGuard-YOLO在OPIXray和HiXray数据集上显示出显著的改进.
在平均平均精度上实现了2.3%和1.6%的增长 (mAP@0.5).
提醒率提高了4.5%和2%,F1得分分别提高了2.3%和1%.

结论:

在复杂的X射线背景中,ScanGuard-YOLO有效地提高了禁止物品的检测.
拟议的网络架构为改善安全选系统提供了一个有希望的解决方案.
该模型显示了在真实世界的安全场景中广泛的应用前景.

关键词:

图像X射线图像X射线图像X射线图像X射线图像这是YOLOv5的.深度学习是一种深度学习.多级特征聚变的多级特征聚变禁止物品检测检测被禁止物品的检测

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