基于轻量级YOLOv8的X射线安全走私物品识别技术的研究
Yaohui Fang1, Chang Xu2, Yulai Zhang1
1School of Information and Electronics Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China.
Scientific reports
|October 24, 2024
概括
一个新的轻量级算法,YOLO-CBF,通过优化YOLOv8.8来增强X射线走私物品检测. 这种方法提高了准确性,并降低了安全选的计算成本.
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:
- 在走私物品的识别和定位方面,X射线安全查面临着挑战.
- 在高峰运营期间,升级安全设备至关重要.
研究的目的:
- 开发一个轻量级的X射线安全走私物品检测算法,优化为YOLOv8.
- 为了提高在X射线图像中偷运物品检测的效率和准确性.
主要方法:
- 减少网络通道数量,以减少模型参数和计算复杂性.
- 引入了一种简化的双向特征金字塔网络 (BiFPN),用于多尺度的特征集成.
- 开发了焦点-GIoU Loss,以解决样本不平衡,并专注于困难的目标.
主要成果:
- 在PIDray数据集 (1.5%,0.9%,0.5%) 上实现了改善的平均精度 (mAP).
- 将参数数量从3.0M降低到2.1M,计算成本从8.1G降低到6.3G.
- 在比较和跨数据集概括实验中表现强.
结论:
- 拟议的YOLO-CBF算法为X射线走私物品检测提供了有效和高效的解决方案.
- 优化提高了检测准确度,特别是在具有挑战性的目标,如小物品或隐藏物品.
- 轻量级的设计使安全设备的部署和升级更容易.
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