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一个实时检测模型,用于在有边缘设备的谷物容器中检测烟雾
Hang Yin1,2, Mingxuan Chen2, Yinqi Lin2
1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
Heliyon
|August 18, 2023
概括
这项研究引入了一个压缩的人工智能模型,用于在谷物桶中早期检测火灾,显著减少模型大小,同时保持高准确度,以便快速识别烟雾并防止食物损失.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 消防安全工程 消防安全工程
背景情况:
- 全球粮食安全受到粮食危机日益严重的威胁.
- 谷仓火灾对粮食损失做出了重大贡献,需要有效的早期检测方法.
研究的目的:
- 开发和部署一种模型压缩技术,以便快速检测谷物容器中早期的火灾.
- 为了应对检测初始火灾的微小和细微烟雾标志的挑战.
主要方法:
- 通过在谷物桶中的实验获得专门的烟雾数据集.
- 使用YOLO v5s开发一个实时检测模型,结合稀疏训练,通道修剪和微调.
- 将优化模型部署到各种边缘设备上进行实际应用.
主要成果:
- 拟议的模型实现了94.90%的平均平均精度 (mAP) 在谷物容器中检测烟雾.
- 实现了模型大小大幅减少5.11 MB.
- 该模型在标准设置上显示了109.89 FPS的高检测速度,在边缘设备上显示了49.26 FPS,从而实现实时监控.
结论:
- 该模型压缩技术有效地检测到谷物桶中的早期烟雾,这对于减轻与火灾相关的食物损失至关重要.
- 优化的YOLO v5s模型为农业环境中的边缘设备的实时火灾检测提供了可行的解决方案.
- 这种方法有助于增强粮食安全,最大限度地减少粮食库火灾造成的损失.
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