Research On Intelligent Recognition Method Of Mine Belt Fire Video Image

As one of the main forms of coal mine fires,mine belt fires cause immeasurable losses.Therefore,the realization of intelligent identification of mine belt fires is of great significance to ensure the safety of mine workers.With the improvement of the level of mine automation,the traditional belt fire identification method has been unable to meet the current intelligent needs of the mine,and the fire video image recognition method based on deep learning has the advantages of fast response and high accuracy,and its application to mine tape fire recognition has certain practical value for improving the safety factor of mines.This paper studies the problems existing in the application of deep learning technology to mine belt fire identification are studied as follows:Aiming at the problems of color distortion and low chromaticity caused by the complex environment in the mine,the proposition about dark channel dehazing algorithm is improved based on K-means which is to process the image and increase the readability of the image.Then,the feature of image will be more rich.At the same time,the dynamic pyrotechnic information in the well is extracted by the fusion algorithm of the frame difference method and the Gaussian mixture model,which reduces the interference caused by the static non-target similar to the pyrotechnic target and improves the identification accuracy of the belt fire.In view of the problems of low belt fire recognition accuracy and complex calculation parameters of the AlexNet model,small-sized convolution kernels are used to replace the large-sized convolution kernels in the first two layers of the AlexNet model to obtain richer feature information and improve the recognition accuracy of the model and training speed.Meanwhile,the Relu activation function replaced by the Mish function.Then,the generalization ability of the model is improved.According to the experimental results,the recognition accuracy of the improved AlexNet belt fire recognition model can reach 88.3%,which is 5.9%higher than that before the improvement.Performance has been effectively improved.In view of the low accuracy and long training time of the YOLOv4(You Only Look Once,YOLO)belt fire recognition model,this paper combines the dynamic attention mechanism and group normalization to improve the original model to avoid losing information and increased the model’s recognition accuracy.At the same time,the Cross stage partial network(CSP)and Spatial pyramid pooling(SPP)modules of the original algorithm model are optimized by using the deep separable convolution network and random pooling.The map of the improved F-YOLOv4 belt fire identification model has reached about 97.3%after experimental verification,which is 7.85%higher than the original model,the model has certain practical value for the identification of mine belt fire.