Objective  The frequent occurrence of forest fire brings great difficulty to forest prevention and control. Traditional forest fire identification algorithms have problems such as low accuracy and insufficient processing efficiency. At the same time, forest fire image data itself has strong complexity. It is necessary to comprehensively consider the recognition accuracy and generalization ability. For this reason, this paper uses the Sparse-DenseNet model to carry out the identification research of forest fire.

  Method  Firstly, the DenseNet model was sparsely transformed, and the sparse effect was generated by randomly shielding the nodes in the Dense Block module, so that the algorithm had the advantages of reducing over-fitting, mitigating gradient disappearance and accelerating convergence speed. Secondly, when image acquisition was carried out in the forest area, due to the relative motion between the camera device and the object being collected and the effect of light and shadow, the image data may be disturbed. Therefore, this paper uses python image processing tools to transform the image. The image dataset was expanded accordingly to fit the actual application scenario. Finally, this paper compares the performance of the Sparse-DenseNet model with other classical deep learning models on the forest fire dataset and the cifar10 dataset, and observes its effects.

  Result  The Sparse-DenseNet model had the characteristics of being lighter in structure, training faster, better effect of avoiding over-fitting. It had a good performance in the forest fire data set and the standard data set cifar10.

  Conclusion  The Sparse-DenseNet model proposed in this paper can effectively optimize the problems existing in traditional forest models and achieve good recognition results. The accuracy rate can reach 99.33%, which is better than DesenNet’s 98.15%, and the training time is only about three-quarters of DenseNet in same round.