Research On Smoke And Flame Recognition And Detection Based On Deep Learning

The smoke and flame caused by fire disaster have posed a great threat to the living environment of human beings.Sensor-based detection methods have some problems such as uneconomical,delay and false positives.However,the traditional image processing methods are not robust enough,which are very harmful to the early warning of the fire disaster.In recent years,deep neural network model has made remarkable achievements in the visual domain,and the application of the deep network model in the recognition and detection of smoke and flame has become a research hotspot.However,due to the disadvantages of high storage and high power consumption in the deep network model,the recogonition and detection of smoke and flame in the resource limited environment will be greatly hindered.Therefore,this paper uses lightweight and channel pruning methods to effectively compress and accelerate the model,and the main research work of this paper can be summarized as follows:(1)This paper uses a lightweight network structure mobilenetv2 to accurately and quickly recognize smoke and flame images.The network uses a large number of 1*1 convolution structure and depthwise convolution structure to reduce the amount of model parameters and calculations,while maintaining a high recognition accuracy.The experimental results in the smoke and flame datasets show that compared with the large-scale network VGG16,the mobilenetv2 network model reduces 84.9%parameters and 98.0%FLOPs,while the recognition accuracy is 0.76%higher.(2)This paper proposes an improved refinedet model to accurately and quickly detect smoke and flame images.This model replaces the basic network VGG16 in the original refinedet model with mobilenetv2,and the additional layer is reduced to one convolution layer.At the same time,the convolution module in the TCB structure is replaced by two inverted residual block modules,so as to achieve a large scale of model compression and maintain a high detection accuracy.The experimental results in the smoke and flame datasets show that compared with the original model,the improved model method reduces 87.8%parameters and 97.0%calculations,while the mAP value only decreases 0.4%.(3)This paper proposes an efficient end-to-end channel pruning method to further compress and accelerate the smoke and flame recognition and detection network.This method is mainly to add L1 regular items to the scaling factor and translation factor of the BN layer,and use the FISTA algorithm to complete the automatic pruning of the model,and once again reduce the size of the model.The experimental results in the smoke and flame datasets show that it can prune 40.3%calculations and 36.8%parameters,while the classification accuracy is only lost by 1.72%.When pruning the improved refinedet model,it can save 43.0%calculations and 47.5%parameters,while the mAP value is increased by 0.7%.