Research On Abnormal Visual Detection Method Of Image Defogging And Fully Mechanized Mining Face In Coal Mine

The visual recognition technology of abnormal conditions in fully mechanized coal mining face is a hot research topic in China in recent years.Coal dust and water mist in the coal mine underground environment make the video image fog phenomenon serious,resulting in poor quality of collected images,which is difficult to meet the needs of underground abnormal state monitoring.Therefore,the research on image defogging and visual anomaly detection method of fully mechanized mining face is of great significance and engineering value.In this paper,combined with image defogging,target detection technology,target tracking technology,and action recognition technology,the visual anomaly detection model was established,and the visual detection method of the abnormal condition of the fully mechanized mining face was studied.The image defogging and visual anomaly detection model was transplanted to the embedded platform to realize the abnormal detection of hydraulic support panel,towing cable,large coal,and personnel.The dust fog phenomenon of video in fully mechanized mining faces is serious,and the quality of collected images is poor,which affects the accuracy of visual anomaly detection.In this paper,based on the dark channel prior algorithm,the transmittance of the bright region was optimized to solve the problem of defogging distortion in coal mine images containing bright regions.An improved dark channel prior algorithm was proposed to solve the problem of the poor real-time performance of image defogging by parallel acceleration processing of minimum filtering,mean filtering and histogram statistics,and transposition optimization of column filtering.Experiments on GPU and embedded platform showed that it can meet the needs of real-time defogging and has good defogging effect.Target detection based on deep learning has a large number of parameters and computations,and there is a large consumption of computing resources and memory,which is difficult to deploy on embedded platforms.The YOLOv5s model was optimized by lightweight combined with an attention mechanism to reduce model parameters and improve reasoning speed.AFFPN was used to optimize the original feature pyramid network of YOLOv5s to improve the detection performance of multi-scale targets in a fully mechanized coal mining face.α-CIoU was used to optimize CIoU loss function to improve the detection accuracy of fully mechanized coal mining targets.The improved algorithm was verified by using the public data set and the self-made abnormal data set of a fully mechanized working face.The experimental comparison showed the effectiveness of the algorithm.Abnormal detection of hydraulic support face guard,personnel,and towing cables is a problem.The improved YOLOv5s were used to detect the target of the hydraulic support face guard,personnel,and towing cable to obtain the label and positioning information.The abnormal recognition alarm of the hydraulic support face guard was realized by label combination classification.Judge whether the personnel positioning coordinates in the dangerous area to achieve abnormal identification of personnel intrusion alarm.Determine whether the positioning coordinates of the towed cable can realize the abnormal identification and alarm of the towed cable from the track in the safe area.The effectiveness of the above method was verified by the test of the abnormal data set of a fully mechanized working face.Identification of abnormal behavior of coal retention and blockage is a problem.This paper presented a multi-target tracking algorithm for bulk coal based on DeepSORT with improved YOLOv5s.Within 50 consecutive frames,the maximum distance of continuously tracked large coal was calculated.The distance threshold was set to realize the abnormal behavior recognition of large coal.Experiments on abnormal data sets showed that the retention and blockage state of bulk coal can be accurately identified.Abnormal detection of irregular movement of underground personnel is a problem.This paper constructed Person_Action2021 behavioral anomaly recognition dataset.The human skeleton was extracted by OpenPose,and the skeleton space-time map was constructed.The skeleton space-time map was sent to the space-time map convolution to complete the action recognition and realize the abnormal behavior recognition.Experiments showed that this method can accurately identify abnormal behavior.This paper built an embedded experimental platform for image dehazing and visual anomaly detection.The collected video of fully mechanized coal mining face in underground coal mines was preprocessed by image defogging,and the experimental verification was carried out on the abnormal detection of hydraulic support side guard plate,personnel intrusion detection,cable departure track detection,abnormal recognition of large coal behavior,abnormal recognition of personnel behavior and composite anomaly detection algorithm.The results showed that the proposed image defogging and visual anomaly detection can preliminarily meet the real-time requirements with high accuracy.It can realize the demand for automatic inspection and has certain reference significance for automatic monitoring and decision-making of fully mechanized coal mining faces.