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DSS-YOLO:基于YOLOv8的改进轻量级实时火灾检测模型
Hongjie Wang1, Xiaoyang Fu2, Zixuan Yu1
1School of Computer Science, Zhuhai College of Science and Technology, Ankee Road East, Zhuhai, 519040, Guangdong, China.
Scientific reports
|March 16, 2025
概括
这项研究介绍了DSS-YOLOv8,一个改进的火灾检测模型. 它提高了对小型和隐蔽目标的早期火灾检测准确度,同时降低了计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 消防安全工程 消防安全工程
背景情况:
- 火灾带来重大风险,早期检测受到小火焰,分散的烟雾和受阻物体的挑战.
- 现有的系统经常在复杂的火灾场景中扎着错过检测和糟糕的实时性能.
研究的目的:
- 开发基于YOLOv8n.n.的改进火灾检测模型 (DSS-YOLOv8).
- 提高对隐蔽物体和小目标的识别精度.
- 减少实时应用程序的计算开销.
主要方法:
- 修改了YOLOv8n架构,用DynamicConv取代C2f模块,以减少计算.
- 整合了SEAM注意力机制,以改善隐藏和小目标的检测.
- 集成的SPPELAN模块来增强多尺度检测能力.
主要成果:
- 与原来的YOLOv8n.相比,DSS-YOLOv8实现了mAP的0.6%增加和RECALL的1.6%增加.
- 模型大小和FLOP分别减少了3.4%和12.3%.
- 在各种火灾场景 (室内,森林,建筑) 中表现出更好的性能.
结论:
- DSS-YOLOv8模型为智能火灾监控系统提供了有效的技术支持.
- 显著降低计算成本,增强实时火灾检测能力.
- 促进火灾危险的早期检测,最大限度地减少潜在的损害.
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