Research On The Motion Monitoring Method Of Underground Belt Conveyor Based On Deep Learning

As the intelligent monitoring system plays a more and more important role in coal mine safety production,the motion state monitoring of underground belt conveyor in the monitoring video has become one of the main research directions.The intelligent monitoring of the belt conveyor can not only provide more valuable information for the underground video monitoring work,but also give early warning of the occurrence of abnormal accidents and provide guarantee for the safety production.Therefore,it is very important to accurately monitor the status of belt conveyor in real time.Combined with the characteristics of underground monitoring video,this paper studies the motion state monitoring method of belt conveyor based on deep learning.(1)In order to detect coal more accurately and in real time,a coal detection method combining lightweight network and dual attention is proposed in this paper.Firstly,the image preprocessing method combining automatic exposure correction and adaptive contrast equalization is used to enhance the quality of underground video images.Secondly,MobileNet lightweight network combined with spatial pyramid pooling is used to extract coal features effectively to ensure the real-time performance of the algorithm.Finally,the path aggregation network PANet was combined with the dual attention mechanism CBAM to enhance the sensitivity of the network to the coal and improve the accuracy of the algorithm.The experimental results on the data set of the belt conveyor show that the enhanced image of the preprocessing method presented in this paper is used in the proposed coal detection algorithm,and the detection accuracy is improved by 8.89%.Compared with the coal detection method and Yolov4,the detection speed is improved by 19 frames/s,and the accuracy is-improved by 14.37%.(2)In order to realize the calculation of the real time speed of the belt conveyor,a visual speed measurement method combined with deep learning is proposed in this paper.Firstly,the deep learning target detection algorithm proposed in this paper is used as the detector,and the Kalman filter is used as the tracker to realize real-time tracking of coal in video frames.Secondly,by calculating the two vanishing points in the image,the model of calculating the moving velocity of coal block is established according to the matrix transformation relationship of two-dimensional and three-dimensional coordinate system,so as to monitor the real-time velocity of the belt conveyor.The experimental results on the surveillance video of the belt conveyor show that the proposed target tracking based on deep learning can effectively track coal blocks,and the error of the velocity measurement results is within 0.7m/s compared with the actual value.(3)In general,the method in this paper not only improves the detection and recognition effect of the algorithm on the coal block on the belt conveyor,but also effectively monitors whether the belt conveyor appears no load or abnormal movement state through the tracking and velocity measurement of the coal block,thus realizing the intelligent monitoring of the movement state of the belt conveyor.