Research On Flame Detection Method Of Video Image Based On YOLOv3

As a frequent disaster,fire has seriously threatened people’s lives and property safety.In order to avoid fire,early detection has become a key link.The traditional fire detection method is to analyze and judge the environmental indicators through the sensor equipment,but there are many problems,such as limited detection distance,slow response,high false alarm rate and so on.With the wide application of video monitoring technology and the rapid development of artificial intelligence in the field of video image processing,the flame detection method based on this has begun to form a new breakthrough.However,the existing flame detection methods still have some problems,such as low detection accuracy and poor real-time performance.Aiming at the problems of low definition and high noise of existing flame image data sets,Gaussian filtering algorithm is used to reduce the noise of flame image.Dark channel defogging algorithm based on fast guided filter and adaptive histogram equalization algorithm are used to improve the quality of flame image data sets;The random erasing method and mosaic algorithm are used to enhance the flame data set to improve the generalization ability of the trained model and the accuracy of model detection when the flame target is occluded.Aiming at the problems of low accuracy of existing flame detection models and missing detection of small target flame,combined with feature fusion and attention mechanism,an optimized yolov3 network structure is proposed to improve the detection of small flame and occluded flame.The detection accuracy is improved by reducing the loss of output flame feature information after convolution.The label smoothing algorithm is used to deal with the sample labels to avoid "over believing" the sample labels in the model training;The focal loss function is used as the function in the model training to improve the unbalanced distribution of flame data.The experimental results show that the average accuracy of the improved model is 94.81%,which is 3.75%higher than that of the original model.Aiming at the problem of poor real-time performance of flame detection algorithm caused by large amount of calculation and long forward reasoning time of the model,firstly,the convolution layer of the sparse flame detection model is pruned based on BN layer,and then the shortcut layer of the improved yolov3 flame detection model is pruned by combining the global channel pruning threshold and L1 norm,Further reduce the inference time of input and output between model layers.The experimental results show that the model size is reduced by 95.8%and the detection speed is increased by 66%.In terms of flame detection accuracy and real-time performance,the yolov3 model is optimized.Finally,the flame detection model is verified by field experiments.The results show that the proposed flame detection method can well meet the requirements of video image flame detection accuracy and speed.