在基于YOLO-AR的遮蔽场景下,研究如何提高古代建筑中的火灾检测性能
Chen Li1, Minghan Wang1, Lei Lei1
1School of Fire Protection Engineering, China People's Police University, Langfang 065000, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
一个新的火灾检测算法,YOLO-AR,通过使用注意力模块和专门的损失函数来提高古代建筑的准确性. 这为保护文化遗产提供了可靠的视觉检测解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 保护文化遗产 保护文化遗产
背景情况:
- 由于复杂的场景和结构特征,古代建筑中的火灾检测具有挑战性.
- 传统的方法与堵塞和独特的建筑特征作斗争.
研究的目的:
- 提出YOLO-AR,一个改进的基于YOLOv8的算法,用于在古代结构中准确检测火灾.
- 为了增强火焰和烟雾特征的捕获,并改善封闭场景中的定位.
主要方法:
- 使用了改进的YOLOv8框架,其中包含了卷积块注意模块 (CBAM).
- 引入排斥损失,以优化在密集和封闭条件下的界限框精度.
- 在一个由15847张古建筑图像组成的定制数据集上进行训练和评估.
主要成果:
- 在精度,回忆和mAP方面,YOLO-AR在主流算法上表现出优越的性能.
- 实现了90.7%的检测精度和89.7%的回忆率.
- 在一个专门的古代建筑数据集上表现优于比较方法.
结论:
- YOLO-AR为古老建筑的早期火灾预警系统提供了一种高效可靠的视觉检测解决方案.
- 该算法通过先进的火灾检测,为保护文化遗产做出了重大贡献.
- 该研究强调了注意力机制和专业损失函数在具有挑战性的视觉检测任务中的有效性.
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