测试技术学报2025，Vol.39Issue(2)：121-129,9.DOI:10.62756/csjs.1671-7449.2025024
汽车发动机积碳图像细粒度分类方法研究
Research on Fine-Grained Classification Method of Carbon Deposit Images in Automobile Engines
摘要
Abstract
Because of the poor recognition effect of carbon deposit images of automobile engines in the tra-ditional model and the practical application requirements,a classification method for the fine-grained fea-tures of carbon deposit images is proposed.For the problem that it is difficult to focus on the nozzle region with large morphological differences in the nozzle carbon image classification task,a carbon image recognition method based on the CA mechanism is proposed.The weights of different channels and spa-tial locations are adjusted to improve the focusing effect of the model on key features by adding the CA attention mechanism.To address the problem that it is difficult to fully capture the key spatial features in the task of carbon image classification of the piston top,an adaptive carbon image recognition method based on SG-former is proposed,which effectively extracts the fine-grained features in the key areas by automatically assigning the global attention weights and maximizes the translational invariance of the car-bon features in the downsampling process through BlurPool pooling.Eventually,the accuracy of carbon degree recognition in the injector nozzle area and piston top area reaches 88.52%and 88.20%,respectively.关键词
积碳检测/图像分类/RepVGG/注意力机制/SG-formerKey words
carbon deposit detection/image classification/RepVGG/attention mechanism/SG-former分类
计算机与自动化引用本文复制引用
陈香,陈友兴,张航佳,刘昱彤..汽车发动机积碳图像细粒度分类方法研究[J].测试技术学报,2025,39(2):121-129,9.基金项目
山西省回国留学人员科研资助项目(2022-145) （2022-145）
山西省重点研发计划资助项目(202302020101008) （202302020101008）
山西省研究生科研创新资助项目(2024KY607) （2024KY607）
