Research On Intelligent Recognition Method Of Abnormal State In Fully Mechanized Mining Face Based On Machine Vision

In the production process of fully mechanized mining face,when the shearer cuts coal forward,if the guard plate of hydraulic support near the drum is not normally retracted,the two will collide and produce cutting interference,resulting in damage to fully mechanized mining equipment and even threatening personnel safety.At the same time,large pieces of coal fall on the scraper conveyor due to wall and other reasons,which may block and shut down the scraper conveyor and reduce the working efficiency.With the improvement of intelligent level of coal mine,machine vision is often used to monitor the working face in coal mine.In this paper,the machine vision method is used to study the abnormal state recognition method of hydraulic support side guard and the image recognition method of scraper conveyor large coal.However,serious dust is often produced on the production site,which affects the definition of underground monitoring video image.Therefore,at the same time,the method of clarifying low illumination fog dust image of working face is studied to improve the quality of monitoring video image of fully mechanized mining face.This paper studies the impact on coal mine safety efficient mining is of great significance.Aiming at the problems of serious fog and dust and low brightness in the monitoring image of fully mechanized mining face,a fog and dust image clarity model is established to solve the transmittance function,and the logarithmic transformation is introduced to optimize the transmittance function to improve the image brightness.At the same time,in order to avoid the problem of over illumination of miner’s lamp and other light sources in the image caused by logarithmic transformation,a method of selecting logarithmic transformation multiple according to the brightness peak of the original image is proposed.Compared with a variety of image defogging algorithms widely used at present,the results show that the proposed method has good results in vision and objective evaluation indexes.It is suitable for the low illumination and high fog and dust environment of fully mechanized mining face,and provides high-quality images for the subsequent identification of abnormal states of hydraulic support guard plate and the identification of large coal of scraper conveyor.Aiming at the problem of identifying the abnormal state of the guard plate of hydraulic support,an abnormal state identification method of the guard plate based on deep transfer learning is proposed.This method takes Faster R-CNN deep learning method as the main framework and VGG16 network as the feature extraction network.In order to improve the recognition accuracy,the softmax classifier is replaced by AdaBoost classifier,and the soft NMS method is introduced to replace the NMS method.The parameters and weights of VGG16 network trained in ImageNet data set are transfered to obtain the abnormal state recognition model of the upper guard plate,so as to realize the accurate recognition of the abnormal state of the guard plate of hydraulic support.Aiming at the recognition of large coal of scraper conveyor,the method based on Kernel Fuzzy C-means clustering is used to segment the image of large coal,and the overlapping area of coal is refilled by combining the open operation and closed operation of image morphology operation.Then the pixel size of large coal is counted by the external rectangle marking method,and finally the proportion coefficient is calculated according to the camera calibration projection principle,the pixel size of the external rectangle of large coal is converted into the actual size,and the accurate recognition of large coal is realized.In order to verify the method,the monitoring video of the fully mechanized mining face of Huangling No.2 coal mine is used to test and verify the abnormal state recognition method of the hydraulic support side guard and the large coal recognition method of the scraper conveyor.The results show that the average recognition accuracy of the abnormal state recognition model of the hydraulic support side guard in the field video is 90.02%,the average recognition time is 89.92ms,and the accuracy of the large coal recognition of the scraper conveyor is 90.86%.At the same time,based on the above theoretical methods,the abnormal state image recognition software system of fully mechanized mining face is designed.Using the QT design software system interface,it interacts with Python and OpenCV to realize the functions of clarity of fog and dust video image of fully mechanized mining face,abnormal state detection of hydraulic support sideboard,image recognition of large coal and so on.