Research on Recognition Method of Carbon Deposit Degree Based on Improved EfficientNet-B0

Accurate classification of carbon deposit severity for automotive engines is critical for routine vehicle inspection and exhaust emission pollution control. Although deep learning-based methods have made certain progress in carbon deposit visual detection, they face prominent drawbacks in real industrial deployment because endoscopic carbon deposit images carry subtle inter-class fine-grained textures and lack sufficient labeled training samples, which severely impair conventional models’ classification performance and prevent them from satisfying the accuracy requirements of practical inspection tasks. This paper proposes an improved EfficientNet-B0 framework for fine-grained identification of cylinder wall carbon deposit severity. By introducing the DiffuseMix data augmentation algorithm, label confusion and overfitting are avoided. At the same time, a dual-path weighted feature fusion structure (DPWFFS) is designed to achieve bidirectional complementary of shallow and deep features, enhancing the representation ability of local discriminative features of carbon deposits. Quantitative experiments conducted on our self-built cylinder wall carbon deposit dataset demonstrate that the proposed improved model achieves a classification accuracy of 90.33%, which substantially outperforms the original EfficientNet-B0 baseline. The presented method provides an effective technical reference for the intelligent automatic identification of engine carbon deposit severity.