Real-time fire detection method based on deep learning
In response to the issues of low accuracy and long processing time in fire detection,a lightweight real-time fire detection method based on the improved FireNet is designed.The method utilizes video image data to perform fire inference analysis and identification using a network model.Firstly,a multi-scale convolutional network is employed in the FireNet feature extraction stage and a channel attention mechanism is introduced to improve the regression accu-racy.Secondly,the number of neurons in the fully connected layer is compressed and optimized to reduce computational time.Experimental results show that the improved FireNet algorithm achieves a detection accuracy of 96.43％,with a model storage space of 0.96MB and a detection frame rate of 40.Compared to the standard algorithm,the improved method exhibits a 2.5％increase in accuracy,an 85％reduction in storage space,and a 40％improvement in detection frame rate.
fire detectionconvolutional neural networkmulti-scale convolutional networkat-tention mechanism
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