优化YOLOv7微型模型用于电力传输线路中的烟雾检测
Chen Chen1, Guowu Yuan1,2, Hao Zhou1,2
1School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
这项研究引入了改进的YOLOv7微型模型,用于更快,更准确地检测电线附近的烟雾. 增强型号显著提高了检测性能,这对电力系统安全至关重要.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 电力传输线路附近发生的火灾事件存在重大安全风险.
- 由于复杂的场景,现有的烟雾检测模型缺乏准确性和速度.
- 快速准确的烟雾检测对于电力系统的运行安全至关重要.
研究的目的:
- 为高压输电线路开发一个改进的烟雾检测模型.
- 与现有方法相比,提高检测准确度和速度.
- 确保电力系统运行的安全性和可靠性.
主要方法:
- 使用粒子系统生成的烟雾组合成真实场景构建了一个专门的数据集.
- 在YOLOv7-tiny.中引入了无参数关注模块和Spd-Conv (空间到深度卷积).
- 员工通过对合成数据进行预训练和对现实世界的场景进行微调来转移学习.
主要成果:
- 在烟雾检测中,平均平均精度 (mAP) 提高了2.61%.
- 与原来的YOLOv7小型模型相比,精度提高了2.26%,回忆率提高了7.25%.
- 与其他物体检测模型相比,证明了更高的准确性和速度,并在Figlib数据集上提高了性能.
结论:
- 改进的YOLOv7微型机型为电力传输线路提供了增强的烟雾检测功能.
- 该方法有效地解决了现有烟雾检测系统的局限性.
- 这一进步有助于提高电力基础设施的安全性和运行完整性.
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