多策略改进YOLOv11的煤识别方法的多策略改进
Hongjing Tao1, Lei Zhang1, Zhipeng Sun1
1School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究介绍了EBD-YOLO,一种改进的煤炭检测模型,可以提高矿山的准确性和实时性能. 新模型显著减少了错过和错误检测,提高了整体采矿安全和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 采矿工程 采矿工程 采矿工程
背景情况:
- 目前的煤探测方法的准确性很低,检测错误,实时性能差.
- 复杂的采矿环境,低照明和混合材料对现有的检测系统构成重大挑战.
研究的目的:
- 开发一种先进的煤探测模型,EBD-YOLO,克服当前方法的局限性.
- 为了提高在具有挑战性的采矿条件下煤炭的准确性,回忆和实时检测能力.
主要方法:
- 基于YOLOv11n的提议EBD-YOLO模型,结合了C3k2-EMA模块和EMA的注意力,以增强特征提取.
- 集成BiFPN模块可降低计算复杂性,丰富语义和详细信息.
- 使用DyHead探测器头来改善复杂环境中的特征表达.
主要成果:
- EBD-YOLO的精度 (P) 为88.7%,回忆 (R) 为83.9%,平均精度 (mAP@0.5) 为91.7%.
- 与原始模型相比,显著改进,P增加了3.4%,R增加了3.7%,mAP@0.5.5.增加了3.9%.
- 实现了每秒 (FPS) 增加10.01%,这表明了卓越的实时检测性能.
结论:
- 与主流YOLO算法相比,EBD-YOLO提供了优越的检测性能,实现了最高的mAP@0.5和出色的检测速度.
- 有效地解决了关键问题,如错过检测,错误检测和复杂的煤矿环境中的实时检测需求.
- 代表了用于改进采矿操作的自动化煤炭道检测的重大进步.
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