基于改进的Yolov8nn,研究了下孔钻井目标检测
Jierui Ling1, Zhibo Fu1, Xinpeng Yuan2
1School of Coal Engineering, Shanxi Datong University, Datong, 037000, China.
Scientific reports
|July 18, 2025
概括
一个改进的算法增强了地下煤矿钻井现场的实时监控和目标检测. 这种先进的系统提高了精度和平均精度 (mAP),同时减少了模型大小和参数,以实现更安全的采矿操作.
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:
- 地下煤矿钻探对于管理天然气,水和地质危险至关重要.
- 有效的实时监控和目标检测对于提高煤矿安全和运营效率至关重要.
研究的目的:
- 开发一种改进的算法,用于在地下煤矿钻井现场实时检测目标.
- 提高识别关键钻井现场组件和人员的效率和准确性.
主要方法:
- 基于Yolov8n的改进算法被提出,将C2f_PKI模块纳入脊柱和FDPN/DASI融合模块在头部.
- 整合了Focaler_MDPIoU损失函数和MLCA注意力机制,以改善特征融合和焦点.
- 该模型被训练来检测五个关键类别:抓器,钻机,煤矿工,矿山头盔和钻井管道.
主要成果:
- 改进后的模型实现了14%的储存大小减少和17%的参数下降,与原始相比.
- 精度提高了1.4%,平均精度 (mAP) 增加了0.9%.
- 该算法在复杂的地下环境中展示了改进的识别精度和概括能力.
结论:
- 拟议的算法为在具有挑战性的采矿条件下地下目标识别提供了重要的技术进步.
- 这些改进有助于加强煤矿灾害预防和控制能力.
- 这项研究提供了一种更有效,更准确的解决方案,用于实时监控地下煤矿钻井操作.
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