As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.

中文翻译：

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轻量级学生LSTM，用于实时野火烟雾检测

随着对野火检测的需求的增加，结合低成本照相机和深度学习技术的野火烟雾检测的研究也在增加。基于相机的野火烟雾检测价格便宜，可以快速检测，并且可以用肉眼检查烟雾。但是，由于监视系统必须仅依靠视觉特征，因此它经常错误地将雾和云检测为烟雾。在这项研究中，结合使用了一次只看一次检测器和长短期记忆（LSTM）分类器，以通过反映野火烟雾的时空特征来提高野火烟雾检测的性能。但是，由于有必要减轻繁重的LSTM模型的实时烟雾检测，在本文中，我们提出了一种将师生框架应用于深度LSTM的新方法。通过这种方法，浅学生LSTM被设计为减少构成LSTM模型的层和单元的数量，同时保持原始的深LSTM性能。实验结果表明，我们提出的方法与教师LSTM相比，最多减少了8.4倍的参数数量，并缩短了处理时间，同时使用几种最新方法保持了与深LSTM相似的检测性能在野火基准数据集上。