基于红外热成像技术和改进的YOLOv5s模型的暗水环境中人员检测
Liang Cheng1,2, Yunze He3, Yankai Mao3
1School of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种改进的YOLOv5s算法用于夜间水中救援,增强了对个人的红外热成像检测. 这种新的方法达到85.49%的准确性,帮助智能救生任务.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 热成像是一种热成像技术.
背景情况:
- 夜间的水中救援对发现个人提出了重大挑战.
- 现有的方法缺乏足够的准确性和速度,无法进行有效的实时操作.
研究的目的:
- 开发一种增强的YOLOv5s算法,以使用红外热成像技术在夜间精确检测水中生物.
- 提高水上救援应用的检测系统的稳定性和效率.
主要方法:
- 使用CycleGAN和颜色范围转换创建并增强了一个专门的热图像数据集.
- 将卷积块注意模块 (CBAM) 和幽灵卷积集成到YOLOv5s架构中.
- 应用超参数演变来优化训练参数.
主要成果:
- 改进的YOLOv5s算法在专有数据集上实现了平均85.49%的检测准确性.
- 该模型展示了每秒23.51 (FPS) 的预测速度.
- 与前者算法相比,检测准确度和推断速度的显著改进.
结论:
- 拟议的方法为夜间水上救援提供了高识别能力和稳定性.
- 改进的算法有效地满足智能救生任务的需求.
- 这项研究证实了先进人工智能的潜力,可以改善水上安全和救援行动.
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