基于深度迁移学习的烟雾识别方法

针对传统的基于传感器和图像特征的烟雾识别方法易被外部环境干扰且识别场景单一，从而造成烟雾识别精度较低，而基于深度学习的识别方法对数据量要求较高，对于烟雾数据缺失或数据来源受限的情况模型识别能力较弱的问题，提出一种基于深度迁移学习的烟雾识别方法。将ImageNet数据集作为源数据，利用VGG-16模型进行基于同构数据下的特征迁移。首先，将所有的图像数据进行预处理，对每张图像作随机变换（随机旋转、剪切、翻转等）；其次，引入VGG-16网络，将其卷积层特征进行迁移，并连接预先使用烟雾数据在VGG-16网络中训练过的全连接层；进而构建出基于迁移学习的深度网络，从而训练得到烟雾识别模型。利用公开数据集以及真实场景烟雾图像进行实验验证，实验结果表明，和现有主流烟雾图像识别方法相比，所提方法有较高的烟雾识别率，实验精度达96%以上。

Smoke recognition based on deep transfer learning

For smoke recognition problem, the traditional recognition methods based on sensor and image feature are easily affected by the external environment, which would lead to low recognition precision if the flame scene and type change. The recognition method based on deep learning requires a large amount of data, so the model recognition ability is weak when the smoke data is missing or the data source is restricted. To overcome these drawbacks, a new smoke recognition method based on deep transfer learning was proposed. The main idea was to conduct smoke feature transfer by means of VGG-16 (Visual Geometry Group) model with setting ImageNet dataset as source data. Firstly, all image data were pre-processed, including random rotation, cut and overturn, etc. Secondly, VGG-16 network was introduced to transfer the features in the convolutional layers, and to connect the fully connected layers network pre-trained by smoke data. Finally, the smoke recognition model was achieved. Experiments were conducted on open datasets and real-world smoke images. The experimental results show that the accuracy of the proposed method is higher than those of current smoke image recognition methods, and the accuracy is more than 96%.