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基于YOLOv8n的改进火灾和烟雾检测方法用于智能工厂
Ziyang Zhang1, Lingye Tan1, Tiong Lee Kong Robert1
1School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这项研究引入了一种改进的深度学习模型,用于检测工厂的火灾和烟雾,提高安全性. 增强的YOLOv8n模型达到95%以上的准确性,大大提高了实时监控能力.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业安全 工业安全 工业安全
背景情况:
- 工厂火灾对人类生命和财产构成重大风险.
- 现有的深度学习火灾检测方法往往忽略了工业环境和烟雾检测.
- 智能工厂需要先进的实时火灾和烟雾检测系统.
研究的目的:
- 提出一种基于深度学习的改进方法,用于在工厂同时检测火灾和烟雾.
- 增强YOLOv8n模型,提高工业火灾检测的准确性和速度.
- 开发一个强大的系统,用于智能工厂火灾灾难监测.
主要方法:
- 为培训和验证,创建了一个超过5000个工厂火灾和烟雾图像的定制数据集.
- 作为基础模型,选择了YOLOv8n物体检测算法.
- 架构修改包括集成ConNeXtV2,RepBlock和SimConv,并用MPDIoU取代CIoU损失.
主要成果:
- 改进的YOLOv8n模型在定制数据集上的其他九个先进算法相比表现出了更好的性能.
- 废弃性研究证实了精度,回忆,F1得分和mAP@50.50.的显著改善.
- 修改后的算法实现了超过95%的准确度指标,mAP@50达到95.6%,比原始模型提高4.5%.
结论:
- 提议的增强型YOLOv8n模型为工厂实时火灾和烟雾检测提供了高度准确和高效的解决方案.
- 这些修改有效地提高了特征提取,计算效率和界限框准确性.
- 这一进步有助于增强智能工厂的安全和监控能力.
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