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基于优化的YOLOv5的室内火灾和烟雾检测
Md Shafak Shahriar Sozol1, M Rubaiyat Hossain Mondal1, Achmad Husni Thamrin2
1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
PloS one
|April 29, 2025
概括
一个新的超参数优化的YOLOv5模型显著提高了室内火灾和烟雾检测的准确性. 这种先进的系统提供实时监控和可靠的危险通知,性能优于现有方法.
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:
- 传统的火灾检测系统会受到错误报警和缓慢响应的影响.
- 目前的深度学习对象探测器缺乏动态室内火灾场景的准确性和实时跟踪.
研究的目的:
- 为室内环境开发一个准确和实时的火灾和烟雾检测模型.
- 用先进的深度学习技术解决现有检测方法的局限性.
主要方法:
- 使用遗传算法开发了一个超参数优化的YOLOv5 (HPO-YOLOv5) 模型.
- 创建了5000个室内火灾和烟雾图像的新型数据集.
- 集成YOLOv5与DeepSORT进行实时对象跟踪,并使用Grad-CAM进行模型解释.
主要成果:
- HPO-YOLOv5模型实现了92.1%的平均平均精度 (mAP@0.5),超过了最先进的模型.
- 与基线YOLOv5模型相比,表现出2.4%的改善.
- 该系统提供可靠的实时监控和准确的火灾危险通知.
结论:
- HPO-YOLOv5型号为室内火灾危险检测提供了可靠和有效的解决方案.
- 这项研究为智能消防安全系统的未来进步奠定了基础.
- 该研究强调了优化深度学习模型和可解释AI在关键安全应用中的重要性.
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