To meet the needs of embedded intelligent forest fire monitoring systems using an unmanned aerial vehicles (UAV), a deep learning fire recognition algorithm based on model compression and lightweight requirements is proposed in this study. The algorithm for the lightweight MobileNetV3 model was developed to reduce the complexity of the conventional YOLOv4 network structure. The redundant channels are eliminated through channel-level sparsity-induced regularization. The knowledge distillation algorithm is used to improve the detection accuracy of the pruned model. The experimental results reveal that the number of model parameters for the proposed architecture is only 2.64 million—compared with YOLOv4, this represents a reduction of nearly 95.87%. The inference time decreased from 153.8 to 37.4 ms, a reduction of nearly 75.68%. Our approach shows the advantages of a model with a smaller number of parameters, low memory requirements and fast inference speed compared with existing algorithms. The method presented in this paper is specifically tailored for use as a deep learning forest fire monitoring system on a UAV platform.

为了满足使用无人机的嵌入式智能森林火灾监控系统的需求，提出了一种基于模型压缩和轻量级需求的深度学习火灾识别算法。开发了用于轻量级MobileNetV3模型的算法，以降低常规YOLOv4网络结构的复杂性。通过通道级稀疏性引起的正则化消除了冗余通道。知识蒸馏算法用于提高修剪模型的检测精度。实验结果表明，与YOLOv4相比，所提出的体系结构的模型参数数量仅为264万个，减少了近95.87％。推理时间从153.8毫秒减少到37.4毫秒，减少了近75.68％。与现有算法相比，我们的方法显示了具有较少参数数量，低内存需求和快速推理速度的模型的优点。本文介绍的方法是专门为用作无人机平台上的深度学习森林火灾监控系统而量身定制的。