根据YOLOv8和UAV图像改进了野火烟雾检测
Saydirasulov Norkobil Saydirasulovich1, Mukhriddin Mukhiddinov2, Oybek Djuraev2
1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究通过使用改进的YOLOv8模型与无人机 (UAV) 图像来增强森林火灾烟雾检测. 新型号实现了更高的准确性,这对于早期灭火工作至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 森林火灾是一个主要的全球威胁,需要快速检测才能有效制.
- 目前的烟雾检测方法面临着速度,准确性和识别小烟雾源的挑战.
- 无人驾驶飞行器 (UAV) 为实时森林火灾监测提供了一个有希望的平台.
研究的目的:
- 使用无人机图像开发一个增强的YOLOv8模型,以改进森林火灾烟雾检测.
- 解决现有检测技术的局限性,重点关注速度,准确性和小源识别.
- 提高早期火灾检测系统的精度和可靠性.
主要方法:
- 集成Wise-IoU (WIoU) v3用于增强边界框回归和定位.
- 在部层实施幽灵混合卷积,以减少参数和加速收.
- 引入BiFormer注意力机制,以关注烟雾特征并减少背景噪音.
主要成果:
- 增强的YOLOv8模型实现了平均精度 (AP) 79.4%,比基线提高3.3%.
- 在检测小型烟雾源方面表现出强大的性能,平均精度小 (APS) 为71.3%.
- 达到92.6%的高平均精度大 (APL),表明在各种场景中有效.
结论:
- 增强的YOLOv8模型显著提高了森林火灾烟雾检测的准确性和效率.
- 该模型的组件 (WIoU v3,幽灵混动卷积,BiFormer) 共同促进了卓越的性能.
- 这一进步有助于更有效,更及时的森林火灾管理和灭火.
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