Light fuel classification based on Raman spectroscopy and region-adaptive convolutional neural networks

1 score of 96.12%, all consistently outperforming those of the other models, while maintaining a relatively low parameter complexity of 0.7 M and a short training time of 226.32 s. Further sensitivity analysis validates that the parameters we selected are indeed optimal. It provides a novel and practical solution for rapid battlefield identification of light fuels and offers valuable insights into the design and application of region-aware 1D deep learning models.