Abstract

The detection of carbon deposit degree is of great significance to the maintenance of automobile engine. Due to issues with poor feature aggregation, inter-class similarity, and intra-class variance in carbon deposit data with a small number of samples, model-based discriminative approaches cannot be widely implemented in the market. In order to overcome this technical barrier, the article examines the impact of DCNNs (Deep Convolutional Neural Networks) level on the recognition effect of the degree of carbon deposit, introduces a dropout structure and data enhancement strategy to lower the risk of overfitting brought on by the small dataset, and suggests a recognition method based on the kernel of dual-dimensional multiscale-multifrequency information features to enhance the differentiation characteristic. After experimental testing, the accuracy of this method is 86.9

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Cite this article

@article{cai-6797,
  author  = {Qian Huang and Youxing Chen and Zhaoba Wang and Xiang Chen and Asoke Nandi and Maozhen Li and Yong Jin},
  title   = {Research on Discrimination Method of Carbon Deposit Degree of Automobile Engine Based on Deep Learning},
  journal = {Computing and Informatics},
  volume  = {43},
  number  = {1},
  pages   = {126--148},
  year    = {2024},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2024_1_126}
}
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