煤矿的火灾和烟雾实时检测算法基于改进的YOLOv8s
Derui Kong1, Yinfeng Li1, Manzhen Duan1
1School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan, Hebei, China.
PloS one
|April 18, 2024
概括
这项研究通过优化YOLOv8s模型来增强煤矿的火灾和烟雾检测. 改进的模型提供了实时检测,降低了复杂性和更高的准确性,使其非常适合资源有限的环境.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 采矿安全工程 采矿安全工程
背景情况:
- 有效的火灾和烟雾检测对于煤矿安全至关重要.
- 现有的模型努力平衡准确性和计算复杂性,以便在资源有限的采矿环境中部署.
研究的目的:
- 为煤矿开发一个高效准确的火灾和烟雾检测模型.
- 在计算资源和检测性能方面解决当前模型的局限性.
主要方法:
- 优化了YOLOv8s对象检测模型,在脊椎和部结合了更快的卷曲.
- 将注意力机制集成到脊柱和头部部件中,以提高检测准确度.
- 利用数据增强来提高模型的概括能力.
主要成果:
- 与YOLOv8s.s.相比,模型复杂度减少了23.0%的参数和26.4%的浮点运算 (FLOP),比YOLOv8s.s.减少了23.0%的参数和26.4%的浮点运算.
- 实现了91.0%的平均平均精度 (mAP),比基线模型提高了2.5%.
- 已证明适用于在计算资源有限的环境中实时检测.
结论:
- 提出的方法有效地减少了计算复杂性,同时提高了对火灾和烟雾的检测精度.
- 优化的模型非常适合在煤矿中实时检测火灾和烟雾.
- 这一进步有助于提高采矿业务的安全性.
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