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基于改进的YOLOv8算法检测输送带偏差的研究
Yunfeng Ni1, Haixin Cheng2, Ying Hou1
1Affiliation College of Xi'an University of Science and Technology, Xian, China.
这项研究引入了改进的YOLOv8算法,用于检测输送带偏差. 改进后的模型实现了高精度和速度,使实时监控和减少带故障造成的经济损失.
科学领域:
- 工业自动化 工业自动化
- 机器视觉 机器视觉 机器视觉
- 故障检测系统 故障检测系统
背景情况:
- 输送带偏差是带式输送系统中常见且严重的故障,造成重大经济损失和环境污染.
- 传统的检测方法在故障定位和分析方面缺乏精度,无法满足复杂工业环境中的实时检测需求.
研究的目的:
- 开发一种改进的基于YOLOv8的算法,以高效,准确地实时检测输送带偏差.
- 增强特征提取,特征融合和注意力机制,以提高检测性能.
主要方法:
- 将增强式挤压激发 (ESE) 模块集成到C2f中,以改进滚筒和皮带的特征提取.
- 在子网络中开发BiFPN_DoubleAttention模块,以增强功能融合和注意力.
- 在头部网络中集成多头自我注意 (MHSA) 机制,以捕捉小目标的位置特征.
主要成果:
- 在自建数据集上,获得了98.1%的准确性和99.0%的平均平均精度 (mAP0.5).
- 证明了每秒46 (FPS) 的检测速度,满足实时要求.
- 有效地处理环境变化和干扰,以进行可靠的检测.
结论:
- 建议改进的YOLOv8算法显著提高了输送带偏差检测的准确性和效率.
- 这种方法为工业带式输送系统的实时诊断提供了可行的解决方案.
- 这些进步解决了传统方法的局限性,提供了更好的故障定位和分析准确性.
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