基于深度卷积长短期记忆网络的森林火灾烟雾检测模型

针对采样的每帧烟雾特征具有极大的相似性,以及森林火灾烟雾数据集相对较小且单调等问题,为充分利用烟雾的静态与动态信息来达到预防森林火灾的目的,提出一种深度卷积集成式长短期记忆网络(DC-ILSTM)模型。首先,使用在ImageNet数据集上预训练好的VGG-16网络进行基于同构数据的特征迁移,以有效提取出烟雾特征;其次,基于池化层与长短期记忆网络(LSTM)提出一种集成式长短期记忆网络(ILSTM),并利用ILSTM分段融合烟雾特征;最后,搭建一种可训练的深度神经网络模型用于森林火灾烟雾检测。烟雾检测实验中,与深卷积长递归网络(DCLRN)相比,DC-ILSTM在最佳效率下以10帧的优势检测到烟雾,而且在测试准确率上提高了1.23个百分点。实验结果表明,DC-ILSTM在森林火灾烟雾检测中有很好的适用性。

Forest fire smoke detection model based on deep convolution long short-term memory network

Since the smoke characteristics of each sampled frame have great similarity, and the forest fire smoke dataset is relatively small and monotonous, in order to make full use of the static and dynamic information of smoke to prevent forest fires, a Deep Convolution Integrated Long Short-Term Memory network (DC-ILSTM) model was proposed. Firstly, VGG-16 networks pre-trained on ImageNet dataset were used for feature transfer based on isomorphic data to effectively extract smoke characteristics. Secondly, an Integrated Long Short-Term Memory network (ILSTM) based on pooling layer and Long Short-Term Memory network (LSTM) was proposed, and ILSTM was used for segmental fusion of smoke characteristics. Finally, a trainable deep neural network model was built for forest fire smoke detection. In the smoke detection experiment, compared with Deep Convolution Long Recursive Network (DCLRN), DC-ILSTM can detect smoke with 10 frames advantage under the optimal efficiency and has the test accuracy increased by 1.23 percentage points. The theoretical analysis and simulation results show that DC-ILSTM has good applicability in forest fire smoke detection.