Research On Borehole Wall Detection Information Processing Technology Of Coal Mine

Coal is the main energy source in China.With the development of industrial Internet,computer vision,internet of things,information processing and other technologies,seven national ministries and commissions have listed the construction of intelligent mines as a national development strategy,and it is also an inevitable choice for the digital transformation and development of the coal industry.The transparent geological technology guarantee is based on the mineral geological information data and realizes the clear geological information,which is the important part of the intelligent mine.At present,mostly use manual direct observation of coal mine borehole detection video to judge borehole fractures,which has many problems such as low efficiency,heavy workload,missed judgment and misjudgment,etc.The lithology stratification of geological borehole is also marked and divided by professionals on the interpretation of the natural gamma curve,and the professional requirements are high.This paper has done two parts of research work on information processing of borehole detection:uses deep learning technology to realize intelligent detection of borehole fractures,and uses computer to analyze natural gamma data and integrate depth data for stratigraphic division.In this paper,the digital image processing technology is used to change the position and enhance the image of the video generated data pictures of five different geological boreholes in the upper roof of coal roadway to complete the sample data expansion.Manually annotated 1098 pictures of coal mine boreholes fractures as the dataset,and uses the YOLOv5 algorithm to realize automatic identification of borehole fractures.For the false detection problem,the SENet attention mechanism is added to the YOLOv5 model to improve the ability of the model to distinguish non fractured areas,which improves the detection accuracy by 1.8%compared with the original model,and the average accuracy is increased by 0.9%.For the detection frame regression problem,the effective intersection over union loss function is used to replace the YOLOv5’s complete intersection over union loss function,which improves the position accuracy of detection frame.Compared with the complete intersection over union loss function,the detection accuracy is increased by 1.3%and the average accuracy is increased by 0.4%.For the missed detection problem,the YOLOv5 model anchor frame parameters are optimized and the detection layer is added to improve the model’s detection of small fractured areas.Compared with the original model,the detection accuracy increased by 1.3%,and the average accuracy increased by 0.2%.Finally,combined with the SENet attention mechanism,the effective intersection over union loss function,and the addition of the detection layer,the three-point joint optimization model improves the accuracy of the original YOLOv5 model by 2.1%,the recall rate increases by 1.6%,and the average accuracy increases by 1.0%.The model precision increased by 18.9%,the recall rate increased by 39.5%,and the average precision increased by 28.2%compared with SSD algorithm model.It shows that the optimized and improved model based on YOLOv5 can well identify borehole fractures and meet the needs of borehole fractures detection.Aiming at the problem of noise caused by the underground environment of coal mines and engineering operations during the acquisition process of natural gamma data,the Gaussian filtering algorithm is used to filter and smooth the natural gamma data for denoising.The natural gamma data collected from the roof of the coal roadway is divided by the extreme value variance method of mathematical statistics,and the coal seam,mudstone layer and siltstone layer are divided.In terms of using deep learning to identify lithology,the YOLOv5 algorithm model that can identify coal measure strata is obtained by training 5563 pictures of the roof strata of the coal roadway.Through the depth data to calibrate the position of video data and Natural Gamma data,combined with the video recognition results and the division of Natural Gamma data,the situation of borehole coal seam,mudstone layer and silty sand layer with a depth of 51.12m on the upper roof of coal roadway is comprehensively judged,which provides a certain method and idea for the lithology division of coal mine.