基于深度学习的早期火灾预警算法

传统火灾预警方法存在检测精度低、未发生火灾时不能及时预警的问题, 提出一种基于深度学习的早期火灾预警算法. 首先, 使用红外热像仪采集特定场景中的红外图像, 构建数据集; 其次, 使用改进的YOLOv4算法进行训练得到网络权重, 在主干网络的3个输出特征层后引入卷积注意力模块, 提升网络对关键信息的提取能力; 在主干网络和路径聚合网络中增加卷积层, 提高特征提取的能力; 最后, 使用提出的智能火灾检测(intelligent fire detection, IFD)算法对预测图像处理并根据得分评估火灾隐患. 实验结果表明, 改进YOLOv4算法在数据集上的mAP达到98.31%, 比原始YOLOv4算法的mAP提高了2.7%, FPS达到37.1 f/s, IFD算法精确度为93%, 误检率为3.2%. 提出的早期火灾预警算法具有检测精度高, 未形成火灾时及时预警的优点.

Early Fire Warning Algorithm Based on Deep Learning

Traditional fire warning methods have low detection accuracy and cannot give early warnings in time before the fire starts. Therefore, this study proposes an early fire warning algorithm based on deep learning. Firstly, an infrared thermal imager is used to collect infrared images in a specific scenario for dataset construction. Secondly, the improved YOLOv4 algorithm is applied for training, and the network weights are obtained. The convolutional attention module is introduced after the three output feature layers of the backbone network to improve the ability of the network to extract key information. Convolutional layers are added to the backbone network and path aggregation network to promote feature extraction capability. Finally, the proposed intelligent fire detection (IFD) algorithm is employed to process the predicted image and evaluate the fire hazard according to the score. The experimental results reveal that the mAP of the improved YOLOv4 algorithm on the dataset reaches 98.31%, which is 2.7% higher than that of the original YOLOv4 algorithm, and the FPS is 37.1 f/s; the accuracy of the IFD algorithm is 93%, and its false detection rate is 3.2%. The proposed early fire warning algorithm has the advantages of high detection accuracy and timely warnings when there is no fire.