GCEA-YOLO:一个基于YOLOv11的增强型网络,用于在油田运营区域检测吸烟行为
Qing Liu1, Xiaojing Wan2, Yuzhou Sheng2
1School of Chemical Engineering, Xinjiang University, Urumqi 830049, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
在油田运营中检测吸烟对于安全至关重要. 一个新的GCEA-YOLO网络提高了对不安全行为的检测准确度,大大降低了工业环境中的火灾和爆炸风险.
科学领域:
- 工业安全 工业安全 工业安全
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在油田作业中吸烟带来了严重的火灾和爆炸风险,威胁到人员,生产和环境.
- 目前的检测方法在恶劣的工业条件下对小物体和错误阳性进行精确检测.
研究的目的:
- 开发一种先进的深度学习模型,用于在油田环境中准确检测吸烟行为.
- 克服现有系统在小物体检测和环境干扰方面的局限性.
主要方法:
- 提出了一个新的GCEA-YOLO网络,这是YOLOv11的增强,包含CSP-EDLAN,ADown和EfficientHead模块.
- 实施增强功能学习,空间信息保存和多尺度功能聚合,以实现强大的检测.
主要成果:
- 在GCEA-YOLO网络中,与YOLOv11n.相比,GCEA-YOLO网络表现出了显著的改善.
- 在精度上提高了20.8%,在回忆中提高了6.9%,在平均平均精度 (mAP) 中提高了15.1%.
结论:
- GCEA-YOLO网络提供了一个高度准确和高效的解决方案,用于检测油田运营中的吸烟行为.
- 这一进步有助于提高工业安全,防止由不安全的人类行为引起的潜在事故.
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