时空域深度学习火灾烟雾检测

Spatio-temporal deep learning fire smoke detection

烟雾是火灾早期检测的重要特征。传统机器学习及二维卷积神经网络烟雾检测算法对烟雾特征的提取局限于空间领域,无法提取时域信息。现有的三维卷积神经网络检测算法则存在计算成本高、检测时效低的问题,导致检测准确率和虚警率不理想。针对上述问题,本文提出一种基于时空域深度学习的烟雾视频检测方法。利用分块运动目标检测方法提取烟雾视频的运动目标,过滤非烟雾目标;同时将三维卷积神经网络拆分,形成一种二加一维时空域网络模块,提取时空域特征,提高检测时效。为抑制无关特征,引入注意力机制,增加压缩和激励网络重新标定特征通道权重,提升烟雾检测准确率。研究结果表明,本文所用算法的平均准确率为97.12%,平均正确率为97.06%,平均虚警率为2.74%,平均检测帧率为10.49帧/s,满足火灾烟雾探测需求,检测时效得到明显提高。

Smoke is an important feature of early fire detection.The extraction of smoke features by traditional machine learning and two-dimensional convolutional neural network smoke detection algorithms are limited to the spatial domain,and cannot extract temporal information.The existing three-dimensional convolutional neural network detection algorithm has the problems of high calculation cost and low detection time efficiency,which leads to unsatisfactory detection accuracy and false alarm rate.To solve the above problems,a smoke video detection method based on deep learning in spatio-temporal domain is proposed.The block moving target detection method is used to extract the moving targets of the smoke video and filter the non-smoke targets.At the same time,the three-dimensional convolutional neural network is split to form a two-plus-one-dimensional spatio-temporal network module,which extracts the characteristics of the spatio-temporal domain and improves the detection time efficiency.In order to suppress irrelevant features,an attention mechanism is introduced to increase the compression and incentive network to recalibrate the weight of feature channels to improve the accuracy of smoke detection.The research results show that the average accuracy rate of the algorithm used in this paper is 97.12%,the average correct rate is 97.06%,the average false alarm rate is 2.74%,and the average detection frame rate is 10.49 frame/s.The needs of fire smoke detection is met,and the detection timeliness is improved significantly.