在主动THz安全图像中增强隐藏物体检测,使用适应-YOLO
Aiguo Cheng1,2,3, Shiyou Wu4,5,6, Xiaodong Liu1,2,3
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China.
Scientific reports
|January 21, 2025
概括
本研究介绍了Adaptation-YOLO,这是一种用于检测太赫兹 (THz) 安全图像中隐藏物体的新方法. 它通过结合适应性上下文意识注意力和动态适应性卷积来提高对象检测的准确性和效率.
科学领域:
- 安全技术安全技术.
- 图像处理 图像处理
- 人工智能的人工智能是人工智能.
背景情况:
- 太赫兹 (THz) 安全扫描仪对于非接触式检查和检测危险货物至关重要,有助于反恐工作.
- 当前的对象检测算法由于对象尺寸小,分辨率低和背景噪声,与THz图像作斗争,经常忽视上下文对象依赖性.
研究的目的:
- 开发一种准确有效的方法来检测THz安全图像中隐藏的物体.
- 解决现有的对象检测算法的局限性,以应对THz图像的独特挑战.
主要方法:
- 提出了一个适应性上下文意识注意网络 (ACAN),以在空间和道维度中建模全球上下文特征,融合本地和全球信息.
- 开发了一个动态自适应卷积块 (DACB) 来自适应地调整卷积过器并抑制干扰.
- 将ACAN和DACB集成到YOLOv8中,创建了适应-YOLO模型.
主要成果:
- 适应-YOLO在THz安全图像中检测隐藏物体方面取得了显著的改进.
- 该方法通过建模上下文依赖性和抑制噪音,有效地增强了特征捕获.
- 在主动THz图像数据集上的实验结果证实了拟议方法的准确性和效率的提高.
结论:
- 开发的Adaptation-YOLO模型,集成ACAN和DACB,为THz安全成像中隐藏物体检测提供了强大的解决方案.
- 这一进步有望提高安全查系统的有效性.
- 这项研究强调了在具有挑战性的图像分析任务中,上下文意识的注意力和适应性卷积的重要性.
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