EM-YOLO:一种基于边缘和材料信息融合的X射线禁止物品检测方法
Bing Jing1, Pianzhang Duan2, Lu Chen3
1School of Information and Network Security, People's Public Security University of China, Beijing 102206, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了EM-YOLOv7,这是一种在安全X射线图像中检测禁止物品的改进方法. 它通过有效处理对象重叠和提取安全查关键特征来提高检测准确性.
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:
- X射线成像对于安全检查至关重要,但会带来独特的挑战,如纹理,杂乱和对象重叠.
- 现有的方法与安全X射线图像的独特特征作斗争,阻碍了禁止物品的检测.
研究的目的:
- 开发一种先进的深度学习模型,用于在安全X射线图像中增强禁止物品检测.
- 为了解决当前模型在处理复杂的X射线图像特征和对象重叠方面的局限性.
主要方法:
- 拟议的EM-YOLOv7,集成边缘特征提取器 (EFE) 和材料特征提取器 (MFE).
- 实现软弱非最大抑制 (NMS) 来解决对象重叠问题.
- 将CBAM注意力机制纳入骨干,以改善特征提取.
主要成果:
- 在SIXray数据集上,EM-YOLOv7表现出卓越的性能.
- 与YOLOv5相比,实现了4%的更高检测准确度,与YOLOv7.5相比增加了0.9%.
- 在禁止物品检测方面表现优于其他最先进的 (SOTA) 模型.
结论:
- EM-YOLOv7在安全X射线图像分析方面取得了重大进展.
- 该模型的架构有效地解决了安全查图像的复杂性.
- 这种方法为检测禁止物品提供了更准确,更可靠的解决方案.
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