YOLOFM:基于YOLOv5n的改进火灾和烟雾物体检测算法.
Xin Geng1, Yixuan Su2, Xianghong Cao1
1College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450006, China.
Scientific reports
|February 24, 2024
概括
一个新的火灾检测算法,YOLOFM,通过改进特征提取和减少计算复杂性来提高准确性和回忆. 这使得火灾检测更加可靠和高效,即使在资源有限的设备上.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的火灾检测算法在特征提取,计算复杂性和准确性方面扎,限制了它们在资源有限的设备上部署.
- 挑战包括错过和不准确的检测,需要更强大和更有效的解决方案.
研究的目的:
- 开发一个高精度和高效的火灾检测算法,YOLOFM,解决当前方法的局限性.
- 改进多尺度信息集成,减少模型参数,并最大限度地减少冗余计算,以提高性能.
主要方法:
- 开发了YOLOFM算法,使用FocalNext网络与FocalNextBlock以及一个新的QAHARep-FPN架构.
- 引入了一个新的压缩脱头 (NADH),并提出了用于界限盒回归的焦点-SIoU损失.
- 使用LabelImg软件手动标记了一个由18644张图像组成的数据集 (FM-VOC Dataset18644).
主要成果:
- 与基线网络相比,YOLOFM表现出显著的改善:准确度为3.1%,回忆力为3.9%,F1分数为3.0%,mAP50分数为2.2%,mAP50-95.9分数为7.9%.
- 该算法实现了性能和速度之间的平衡,为火灾检测任务提供了可靠的解决方案.
结论:
- YOLOFM有效地克服了当前火灾检测算法的局限性,提供了更高的准确性和效率.
- 提出的方法,包括FocalNext网络,QAHARep-FPN,NADH头部和Focal-SIoU损失,有助于更强大和可部署的火灾检测系统.
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