基于深度边缘特征和梯度约束的实时带偏差检测方法
Xinchao Xu1,2, Hanguang Zhao1, Xiaotian Fu1
1School of Geomatics, Liaoning Technical University, Fuxin 123000, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了使用ResNet18和注意力机制的实时带偏差检测方法. 这种新的方法显著提高了检测速度和准确性,超过了工业应用的现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 现有的皮带偏差检测方法存在识别效果不佳和准确度低的问题.
- 实时监控对于各种工业运输系统的安全性和效率至关重要.
研究的目的:
- 提出一种新的实时带偏差检测方法,以提高准确性和速度.
- 为了改善电流带偏差检测技术的局限性.
主要方法:
- 使用ResNet18带有注意力机制,用于增强皮带边缘的特征提取.
- 采用了上下文分类器和在模型训练期间结构损失的改进梯度方程.
- 采用最小平方法来准确地进行皮带边缘线的装配和偏差值比较.
主要成果:
- 拟议的方法实现了41/秒的速度,优于其他比较方法.
- 与其他四种检测技术相比,显著提高了0.4%至78.8%的精度.
- 实现了F1分数的改善,从0.3%到72%,满足实际工程应用的要求.
结论:
- 开发的实时皮带偏差检测方法在速度和准确性方面提供了卓越的性能.
- 该技术适用于煤矿,物流和运输行业的智能监控和控制.
- 该方法解决了现有方法的局限性,为更可靠的工业监测铺平了道路.
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