根据图像识别改进了一种快速检测汽油清洁剂的方法
Rongshuo Zhang1, Rencheng Zhu1,2, Ming Jia2
1School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China.
ACS omega
|September 25, 2023
概括
这项研究介绍了一种使用计算机视觉和卷积神经网络 (CNN) 来检测矿床图像中的汽油清洗效果的更快方法,从而提高燃油经济性和减少排放.
科学领域:
- 石油工程是石油工程中的一个.
- 环境科学 环境科学
- 计算机视觉 计算机视觉
背景情况:
- 汽油的排放影响汽车的排放和燃油经济.
- 传统的防毒试验耗时且昂贵.
研究的目的:
- 开发一种快速,准确的方法,通过图像分析来检测汽油清洁剂.
- 评估卷积神经网络 (CNN) 对此任务的有效性.
主要方法:
- 使用多通道汽油洗模拟测试 (MGST) 在板上产生沉积图像.
- 开发了一个检测算法系统,使用计算机视觉和CNN来分析沉积物图像.
- 评估了转移学习模型 (Inception-ResNet-V2,Inception-V3,ResNet50-V2) 和一个定制的CNN.
主要成果:
- 改进的MGST方法显著降低了燃料消耗,成本和测试时间.
- 使用Inception-ResNet-V2的检测准确率高达94%,因为其强度而被选中.
- 该CNN模型有效地从沉积物沉积物中提取特征,以准确检测沉积物.
结论:
- 开发的基于图像的CNN方法为汽油清洁剂评估提供了有效和可靠的方法.
- 这项技术有助于环境监管机构管理市场汽油质量并减少污染.
- 建立存储板图像数据库可以进一步提高环境法规的模型性能.
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