基于特征增强的汽车发动机积碳程度识别模型  

Recognition Model of Automobile Engine Carbon Deposits Degree Based on Feature Enhancement

作　　者：张永玲[1] 黄倩 陈友兴[1] 陈香 张航佳 ZHANG Yongling;HUANG Qian;CHEN Youxing;CHEN Xiang;ZHANG Hangjia(School of Information and Communication Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学信息与通信工程学院,山西太原030051

出　　处：《测试技术学报》2024年第3期315-322,共8页Journal of Test and Measurement Technology

基　　金：山西省回国留学人员科研资助项目(2022-145)。

摘　　要：汽车发动机积碳的长期累积容易加速汽车老化,及时检测并清理可以有效延长汽车使用寿命。对此提出了一种基于视觉图像的积碳程度识别方法,首先针对积碳图像数据量小且类别分布不均的问题对数据进行预处理,其次基于积碳图像特征分布范围广及细粒度特点设计了一个特征重采样模块,从空间和通道两个方向加强特征表达,最后设计了一个轻量化的积碳程度识别模型方便检测部署。结果表明,与其他方法相比,该方法具有较高的预测速度,为179帧/s,且测试精度为84.5%,满足实际需求。Long-term accumulation of carbon deposits in automobile engines can easily accelerate the ageing of automobiles.Timely detection and cleaning can effectively prolong the service life of automobiles.In this paper,a carbon deposits degree recognition method based on a visual image is proposed to automatically recognitze the degree of carbon deposits and provide guidance for carbon deposits cleanup.Firstly,to address the issue of small data volume and uneven category distribution in carbon deposits images dataset,data preprocessing is conducted.Secondly,aimed at the wide range of feature distribution and fine-grained characteristics of carbon images,a feature resampling module is designed to improve the feature expression from both spatial and channel directions.Finally,a lightweight carbon deposits degree recognition model is designed to facilitate the detection of deployed.The experimental results demonstrate that compared to other methods,the method proposed in this thesis achieves the highest inference speed of 179 frames/s with a testing accuracy of 84.5%,meeting the needs of the industry.

关 键 词：积碳程度识别 小样本学习 细粒度图像 特征重采样 轻量化模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]