一种基于轻量型神经网络的火情早期预警方法

Fire Fast Warning Method Based on Lightweight Neural Network

为了提高火灾检测效率,基于模型压缩思想提出了一种火情早期实时检测模型FRDnet (Fire rapid detection network),利用低值滤波器修剪策略优化ShuffleNetV2网络,优化后的网络参数量比原网络减少了50%,提高了运算效率。针对检测结果假性的问题,提出了基于阈值判定的预警逻辑,提高了预警的鲁棒性。在公开数据集上的实验结果表明,模型的检测精度达到了95%,检测效率达到了44 fps;预警逻辑使模型能够在火情发生4.5 s内发出报警信号,表明模型在火灾发生早期能够快速准确预警。

In order to improve the efficiency of fire detection,a fire rapid detection network(FRDnet)was proposed based on the model compression idea.The low-value filter pruning strategy was used to optimize the ShuffleNetV2 network.The optimized network parameters were reduced by 50%compared with the original network,improving computing efficiency.Aiming at the problem of false positive detection results,a warning logic based on threshold judgment was proposed to improve the robustness of early warning.Experimental results on public data sets show that the detection accuracy reaches 95%and the detection efficiency reaches 44 fps.The early warning logic enables the model to issue an alarm signal within 4.5 seconds once a fire occurs,indicating that the model can provide rapid and accurate warning in the early stage of fire.