基于改进的YOLOv11和Savitzky-Golay的钻井管道检测和计数
Cheng Cheng1, Xiaoyu Cheng1, Debo Li2
1China Coal Energy Research Institute Co., Ltd, Xi'an, 710054, China.
Scientific reports
|May 14, 2025
概括
本研究介绍了一种智能方法,用于在煤矿中使用改进的YOLOv11模型和Savitzky-Golay平滑计数钻探管道,在复杂的地下环境中实现准确的自动计数.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 自动钻井管子计数对于煤矿中高效的天然气提取至关重要.
- 现有的方法难以应对地下采矿操作的复杂视觉条件.
研究的目的:
- 开发一种智能和准确的方法,用于在煤矿天然气提取中自动计数钻井管道.
- 增强YOLOv11对象检测模型,以提高该特定应用的性能.
主要方法:
- 创建了一个关于地下天然气开采钻井操作的数据集.
- 通过废弃实验,YOLOv11物体检测模型得到了改进和验证.
- 萨维茨基-戈莱 (SG) 平滑应用于界限框面积曲线,以进行精确的计数.
主要成果:
- 改进的YOLOv11模型实现了高精度 (94.7%),回忆 (95.7%),mAP50 (96.6%) 和mAP50-95 (65.8%).
- 提出的方法优于其他物体检测模型,如YOLOv8,YOLOX,更快的R-CNN和CenterNet.
- 在测试视频中对9个钻孔管的自动计数成功与实际计数进行了验证.
结论:
- 开发的智能计数方法有效地应对复杂的地下采矿环境所带来的挑战.
- 这种方法提供了一个强大的技术解决方案,用于在煤矿天然气开采中进行标准化和准确的钻井管子计数.
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