采矿火灾中烟雾识别的透增强图像处理和改进的YOLOv8的融合
Xiaowei Li1, Yi Liu1
1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种增强的图像处理方法,采用改进的YOLOv8模型来准确检测矿山火灾的烟雾. 这项技术在复杂的地下环境中提高了烟雾的可见性,改善了早期火灾检测和预防.
科学领域:
- 计算机视觉
- 人工智能
- 矿山安全工程
背景情况:
- 基于图像的火灾监测对于早期发现至关重要, 但矿山环境面临复杂的背景和模糊的特征等挑战.
- 现有的方法在地下环境中难以准确和稳定,导致错过或错误的检测.
- 烟雾和背景信息的整合,以及不确定的边界,使烟雾识别变得复杂.
研究的目的:
- 在采矿火灾中使用基于图像的技术开发一个准确和强大的烟雾识别方法.
- 解决复杂的地下环境的挑战,包括集成的烟雾和背景信息以及模糊的视觉特征.
- 在具有挑战性的矿山火灾场景中提高烟雾检测的精度和回忆力.
主要方法:
- 提出了一种结合增强图像处理和改进的YOLOv8模型的新方法.
- 使用时空分离和相等距离的图像差异融合方法来抑制背景噪音并增强烟雾清晰度.
- 在YOLOv8m模型中引入了受限制的特征聚焦机制,以更好地捕捉可变尺度的烟雾目标.
主要成果:
- 建议的方法显著改善了信号与噪声的比率和烟雾区域的清晰度.
- 实现大小烟雾目标的平衡和准确检测.
- 与基线模型和类似算法相比,在精度,回忆和平均精度 (mAP) 方面表现出卓越的性能,满足实时检测需求.
结论:
- 在复杂的采矿环境中,用增强的YOLOv8方法有效提高了烟雾识别的精度和稳定性.
- 这种方法在地下采矿中为火灾的早期检测和预防提供了重大进展.
- 该方法为克服传统地面监测技术的局限性提供了可靠的解决方案.
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