基于YOLOv8-EST的煤矿工作面状态检测
He Wang1,2, Guoqing Wu1, Qingjiang Yang1
1College of Electronic and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
Scientific reports
|February 8, 2026
概括
这项研究介绍了YOLOv8-EST,这是一种用于煤矿的对象检测算法,可以增强特征提取并降低计算负载. 它实现了高精度和效率,提高了采矿操作中的安全性和生产率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度学习对象检测正在煤矿中扩展.
- 越来越高的准确性要求在采矿环境中挑战了有限的计算资源.
- 现有的方法往往会增加网络深度和参数,加剧资源限制.
研究的目的:
- 提出一个新的物体检测算法,YOLOv8-EST,优化用于煤矿面部.
- 为了增强特征提取和空间关系建模,同时降低计算复杂性.
- 为了提高检测准确性和效率,用于煤矿的实时应用.
主要方法:
- 在YOLOv8中引入了Swin变压器模块,以增强功能提取.
- 修改了GELU激活功能,以减少计算复杂性和提高训练性能.
- 使用深度学习网络实现相对位置编码,以更好地捕捉空间关系.
- 集成EMA注意力机制,专注于关键的图像区域和相关特征.
主要成果:
- 在定制的煤矿数据集上,YOLOv8-EST在检测准确性和效率方面取得了显著的改进.
- 该算法实现了高精度,同时降低了计算要求和模型大小.
- 对比分析证实了算法的效率和在煤矿采矿场景中的有用性.
结论:
- YOLOv8-EST提供了一个理想的解决方案,用于实时,高效和准确的对象检测煤矿面.
- 该算法有效地平衡了高检测性能与降低计算负载.
- 这有助于提高煤炭开采行业的生产安全和效率.
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