Coal Mine Safety Research Articles

Nondimensional analysis and application of gas desorption and diffusion driven by density gradient in coal particles.

An in-depth understanding of gas diffusion characteristics in coal is of great value to coalbed methane (CBM) production planning and coal mine safety management. However, the mechanism and model of gas diffusion is still unclear, and some methods for determining diffusion coefficients are not accurate enough. Accordingly, a free gas density gradient (FGDG)-driven coal particle gas desorption and diffusion model was established in this work, and numerical solutions were performed via finite difference method (FDM) and dimensionless method. The variation rules of dimensionless gas pressure, gas content, desorption capacity, and desorption rate were obtained. Finally, the application of the dimensionless method in diffusion modeling and diffusion coefficient inversion was discussed. The results show that the dimensionless method can simplify mathematical equation processing and analyze the common phenomena of desorption and diffusion under different parameters. The gas desorption diffusion in coal particles is from the surface to the inside, and there is obvious desorption hysteresis effect. The larger the dimensionless radius or dimensionless time, the smaller the dimensionless gas pressure, gas content, and dimensionless desorption rate. The dimensionless cumulative gas desorption amount increased rapidly first and then tended to flat with dimensionless time. The simulated curve can be easily converted into the variation curves of several different measured parameters, and the diffusion coefficient can be calculated accurately. The prediction curve of the FGDG diffusion model is in good agreement with the experimentally measured data, which verifies its reasonableness. The research content aims to provide some ideas for modeling gas desorption and diffusion behavior.

Prediction of Coal Mine Pressure Hazard Based on Logistic Regression and Adagrad Algorithm—A Case Study of C Coal Mine

Effectively avoiding coal mine safety accidents has always been an important issue in the process of coal mining. In order to predict mine pressure hazard and reduce the occurrence of mine safety accidents, this paper innovatively combines logistic regression and mine pressure hazard prediction to establish a mine pressure hazard prediction model. By standardizing the data, the model improves the reliability of the mine pressure data and reduces the interference of the prediction effect of random errors. Based on the batch gradient descent algorithm and the Adagrad optimization algorithm, the prediction model is solved innovatively, which greatly improves the calculation speed and prediction accuracy of the model. Accuracy rate, precision rate, recall rate, and F1-score were selected as the evaluation indices to evaluate the prediction effect of the Adagrad optimization algorithm to solve the logistic regression model for mine pressure hazard. Compared with the existing classification algorithms, such as SVM and decision tree, the Adagrad optimization algorithm has the highest four indices when solving the logistic regression prediction model, and it takes the least time to predict. The results show that the model can efficiently predict mine pressure hazard. Finally, C Coal Mine was selected as the example for analysis. The prediction function was added to the mine pressure monitoring interface design. The practical application effect is similar to the theoretical verification. The establishment of this model provides a reliable guarantee for the secure and efficient production of coal mines and provides helpful research for the prediction of mine pressure.

Multi-Indicator Early-Warning Model for Mine Water Inrush at the Yushen Mining Area, Shaanxi Province, China

Previously conducted studies have established that the early warning of water inrush is crucial for the prevention and control of mining water catastrophes in the panel. In order to ensure the safety of coal mining, in this paper, monitoring indicators were determined using the sensitivity analysis method, and then a multi-indicator early-warning model for water inrush was established mainly based on the geological data of the Yanghuopan coal mine. The monitoring stage of the early-warning model should be determined according to the distance between the monitoring borehole and the mining position. Then, the development of a water-conducting fracture zone and the fluctuation and stochastic oscillator of monitoring indicators are comprehensively analyzed to determine the early-warning level. A multi-indicator early-warning model was applied to panel 30302 of the Yanghuopan coal mine and panel 1304 of the Zhaoxian coal mine in the Yushen mining areas. The abnormal warning of the water disaster in panel 1304 was sent 3 days before the disaster, which shows the effectiveness of the model. It can provide a reference for the development of an early-warning model for mine water inrush in Yushen mining areas.

An efficient detection of non-standard miner behavior using improved YOLOv8

To prevent coal mine safety accidents caused by non-standard mining operations, higher requirements have been proposed for identifying miners' behavior in surveillance videos. In this study, a single-stage detection model, YOLOv8, is employed, which effectively predicts the center of objects and is more suitable for recognizing miner behavior in complex environments. First, the video is segmented sampled to capture long-term information. Afterwards, deformable convolutions are integrated, applying offset learning to enhance fine-grained feature extraction and simultaneously, the introduction of the parameter-free SimAM improves the saliency mapping of miner behavior. Furthermore, an improved cooperative loss function is used for high-quality prior box regression. To validate the performance of non-standard miner behavior recognition, the proposed approach is conducted on a self-built dataset and achieves a mAP of 95.7 %, accuracy of 95.3 %, and recall of 95.1 % compared to the state-of-the-arts. It maintains an effective balance between computational cost and recognition accuracy.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis.

In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

Research on evaluation of coal mine safety risk based on AHP-MCS coupling TOPSIS

In order to realize the scientific evaluation of coal mine safety risks, the coal mine safety accident report is used as the data sample, and grounded theory is used to explore the causes of coal mine safety risks. At the same time, combined with the existing research results, a coal mine safety risk evaluation index system is constructed. Monte Carlo Simulation (MCS) is used to optimize the analytic hierarchy process (AHP) to determine the index weights. Then, a coal mine safety risk assessment model based on the AHP-MCS coupling technique for order preference by similarity to ideal solution (TOPSIS) is established by combining TOPSIS. Taking Lingquan Coal Mine as an example, the applicability of the model is verified. The research results show that the coal mine safety risk level is orange risk, which is consistent with the actual situation. It also shows the applicability of the model in the field of coal mine safety evaluation and expands new theories for coal mine safety risk evaluation research.

Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine

Aiming at the real-time perception problem of key target objects caused by harsh environmental factors of high dust, low illumination, motion blur, and multi-target mixing in the comprehensive excavation working face of coal mine, a multi-target detection and tracking algorithm based on DDEB-YOLOv5s and StrongSORT is proposed. First, the YOLOv5s model is improved by using C3-Dense module, decoupled head, ECIoU loss function, and weighted bi-directional feature pyramid network to enhance the detection performance of the model in complex backgrounds of coal mine and complete the design of the DDEB-YOLOv5s multi-target detection network. Second, the DDEB-YOLOv5s algorithm is used as a detector and combined with the StrongSORT tracking algorithm to track critical equipment and miners in the complex context of coal mine. Experiments were conducted on the dataset of comprehensive excavation working face, and the experimental results show that the proposed DDEB-YOLOv5s has the best integrated detection performance compared with other YOLO series target detection algorithms, and its mean value of detection accuracy reaches 91.7%, which is 4.9% higher than that of the original YOLOv5s model. In addition, compared to the three tracking models, (YOLOv7-tiny)-(BoT-SORT), YOLOv5s-DeepSORT, and YOLOv8s-Bytetrack, the (DDEB-YOLOv5s)-StrongSORT model has the best tracking performance (with a mean tracking accuracy of 94.2%) and the least number of identifier switches. Finally, the real-time perception method proposed in this study for the key target of the coal mine working face can provide new technical support and effective guarantee for coal mine safety production.

Research on “multi-blind” working mechanism of Internet+ coal mine safety supervision

PurposeEffective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.Design/methodology/approachThe evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.FindingsIt was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.Practical implicationsThe outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.Originality/valueThis research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

Study on the Recognition of Coal Miners' Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision.

The hoist cage is used to lift miners in a coal mine's auxiliary shaft. Monitoring miners' unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners' statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.

Preparation and Performance Study of a CNFs-Reinforced Three-Dimensional Crosslinked Gel for Controlling Coal Spontaneous Combustion

ABSTRACT Mine fires caused by coal spontaneous combustion (CSC) pose a serious threat to the safety of coal mine in China. In order to improve mechanical properties and inhibition abilities of gel in controlling CSC, a CNFs-reinforced three-dimensional crosslinked inhibition gel was prepared. First, a two-dimensional crosslinked network structure was synthesized. Then, the three-dimensional crosslinked network structure was formed. Graft copolymerization was carried out with the chemical inhibitor tea polyphenol (TP). Carbon nanofibers (CNFs) were evenly dispersed and inserted into the three-dimensional crosslinked network structure. XRD and FTIR spectra results indicated that graft copolymerization among the reagents was successful. SEM and EDS analyses showed that the spatial structure became more dense. The filling of CNFs improved the skeleton’s toughness. TGA and DSC experiments revealed that CSC was slowed down and the heat production was reduced. By in situ FTIR, it was found that the gel interrupted the production of active groups in chain reactions. CO production of the inhibited sample was significantly reduced. The inhibition rate was as high as 74.2%. Finally, pollution was reduced. Therefore, composite gel reduces environmental pollution, improves the toughness of the material, and enhances wrapping performance. It is an environmentally friendly and efficient fire extinguishing material.

Increasing Safety in an Underground Coal Mine through Degasification by Vertical Wells—Influence of the Relationship between the Permeability of Carbon and the Filter Cake of the Bentonite Suspension

The Hullera Vasco Leonesa (HVL) underground coal mine in northern Spain is subject to violent methane (CH4) outbursts. Vertical wells are used to extract CH4 from coal layers to improve mine safety. Bentonite suspensions are used as drilling fluids in this degasification system. The relationship between the soil and filter cake permeabilities, the filter cake thickness, and the filtrate loss significantly affects the fluid’s rheological properties. Fann mud balance, marsh funnel viscometer, and Fann 300 press filter tests have been carried out to determine the rheological properties of the bentonite suspension. A drilling fluid study was carried out for three drilling zones (across which the rheological properties of the drilling fluid vary for the reasons mentioned above): Zone 1, the lower zone, wherein drilling cuts through the coalbed; Zone 2, the upper zone, wherein drilling cuts through layers of sand; and Zone 3, an intermediate zone consisting mainly of rock. When drilling cuts through the coalbed, the release of methane, which improves the safety of underground operations, depends on the relationship between the permeability of the coal and the permeability of the filter cake of the drilling fluid. The effect of sand contamination increases the filtrate loss, and therefore also increases the permeability of the filter cake. The filtrate reducer decreases filtrate loss by recovering the permeability.

Comparative Study on Macroscopic Parameters of Coal Spontaneous Combustion Under Different Test Methods

ABSTRACT The quantitative characterization and the accurate determination of the degree of danger of coal spontaneous combustion (CSC) characteristics are the hotspots and difficulties in the field of coal mine safety. This study compares the test results obtained from the Large-scale CSC experiment and the Kilogram-level CSC experiment and preliminarily determines the main macroscopic characterization parameters of CSC in Huangling No.2 well (including characteristic temperature, spontaneous combustion period, oxygen consumption rate and heat release intensity). At the same time, the various rules of the limit parameters of CSC (including the minimum float coal thickness, the minimum oxygen concentration and the maximum air leakage intensity) of the two experimental results are compared and analyzed. The results show that compared with the Large-scale CSC experiment, the Kilogram-level CSC experiment has the characteristics of rapid and accurate determination of macroscopic characterization parameters of CSC. Therefore, it can be used for the test, the accurate determination of characterization parameters and the determination of the tendency of the CSC process. It is of great significance to the prediction and the risk of CSC.

Exploration and application of microbial method to enhance the effect of hydraulic fracturing on coal seam permeability enhancement and gas extraction

The efficient extraction of gas from low-permeability coal seams is an urgent problem in coal mine safety production. The traditional gas extraction technology generally suffers from problems that limited penetration enhancement or extraction effect, low construction efficiency, large workload, etc. Thus, it is especially urgent and important to explore the new technology applicable to efficient underground gas extraction. In this paper, based on the principle of hydraulic fracturing to increase permeability, we innovatively propose a technique to enhance the effect of hydraulic fracturing to increase permeability and further improve the efficiency of gas extraction using the gas desorption activity of native microorganisms in coal seams. Herein, the composition of the primary microbial community of a coal seam in Xinji No.2 mine was analyzed by bacterial and archaeal 16SrDNA amplicon sequencing, the community structure of the main functional microorganisms was clarified, the optimal combination of functional microorganisms for organic matter degradation in coal seam under anaerobic culture conditions was obtained. Besides the Biolog microplate technology was used to screen the nutrients of the excitation carbon source to stimulate the rapid decomposition of coal organic matter by microorganisms and to define the optimal ratio of the excitation carbon source to microorganisms. Finally, the effect of this technology on the application of coal seam fracturing and gas extraction was tested through field industrial tests, revealing that the extraction effect of this technology was more significant than that of the common coal seam perforation extraction technology. The results of this paper provide a new technical idea for gas extraction from low permeability coal seams, which is an important reference value for subsequent similar studies.

Energy Accumulation Law of Different Forms of Coal–Rock Combinations

Coal–rock disasters are becoming more and more severe as the intensity of coal mining increases. Due to its destructive power and resulting extensive area damage, rock burst is among the most critical threats to coal mine safety. It results from the combined action of the coal and the rock when affected by the mining process. To this end, we used a combination of coal and rock to conduct our studies. Combining a uniaxial compression experiment with theoretical analysis, this work investigated how different lithologies and coal–rock height ratios affect the mechanical properties of this combination and the law governing energy accumulation. We determined the following: When the coal–rock height ratios are dissimilar, the peak strength and modulus of elasticity of the combination show a negative correlation with the coal thickness share, and the pre-peak energy accumulation and impact energy index of the combination is positively correlated with the coal thickness percentage. In combination with the same coal–rock height ratio, the peak strength, elastic modulus, pre-peak energy accumulation, and impact energy index all increase with increased rock strength and elastic modulus. The presence of a hard rock layer affects the accumulation of pre-peak energy. Based on the experimental analysis, a theoretical model was established, and the surrounding rock stress negatively correlates with the percentage of coal thickness; the energy stored in the surrounding rock is directly proportional to the coal in the zone. Therefore, we inferred that the stress distribution of the surrounding rock as coal thickness changes is abnormal; substantial energy accumulation can swiftly initiate dynamic disasters, such as rock bursts. This study has important reference significance for preventing and controlling rock bursts in areas where coal thickness changes.

Study on Spontaneous Combustion Characteristics and Early Warning of Coal in a Deep Mine

Due to high stress, high ground temperature, high moisture, and other factors in deep mines, the risk of coal spontaneous combustion (CSC) is enhanced, seriously affecting the safety of coal mining. To achieve early prediction of spontaneous combustion in the No. 3 coal seam at the Juye coalfield in the deep mine, this paper employs a temperature-programmed device to analyze the changing pattern of single-index gases and composite gas indices with temperature derived from the gas produced during csc. It also optimizes the index gas of coal sample spontaneous combustion. Simultaneously, the characteristics of coal temperature and a four-level warning indicator system for CSC are determined based on the analysis of indicator gas growth rate method, carbon-to-oxygen ratio, and the characteristics of the indicator gas. The composite index gases of the No. 3 coal seam in Juye coalfield are selected in the initial oxidation stage (Rco), accelerated oxidation stage (R1, G1), intense oxidation stage (R2, G1, G3), and oxidative decomposition stage (G3). This leads to the construction of a six-level warning system consisting of initial warning value, blue, yellow, orange, red, and black levels. Meanwhile, warning thresholds are also established.

Development of methane leakage telemetry system based on tunable diode laser absorption spectroscopy

A methane telemetry system based on Tunable Diode Laser Absorption Spectroscopy (TDLAS) is proposed for the long-range, high-precision, and real-time measurement of methane concentration in coal mine safety production. The output wavelength of the tunable semiconductor laser is controlled to scan at 1654nm (6045.94cm−1) of the methane absorption spectrum. The harmonic signal is obtained by digital phase locking technique, and the effect of light intensity variation on the system measurement results is eliminated by 2f/1f signal processing technique. Through calibration experiment and measurement error analysis, the measurement error of the system is less than 10%, which proves that the device has the advantages of high accuracy.

Spatial and temporal convergence of provincial coal mine safety accidents and production and their influencing factors: Data from 23 coal-producing provinces in China (2005–2021)

As a high-risk industry, coal mine safety accidents can create significant loss of life and property to the nation and its people. In this work, a nonlinear time-varying factor model was adopted to investigate the spatial and temporal variation rule of coal mine safety accidents and coal production in 23 provinces of China from 2005 to 2021. The driving factors of coal mine safety accidents in 2005, 2010, 2015 and 2020 were analyzed using the geographical detector method. We further obtained the explanatory power of the primary factors and the change process of interaction. The findings showed that there are convergence clubs for both the number of coal mine safety accidents and coal production in the Chinese provinces. However, the geographical distribution of the convergence clubs varied. The geographical detector results indicated that coal mine safety accidents are affect by a combination of factors. The explanatory power of each factor for coal mine safety accidents changed over time. Notably, the explanatory power of coal production for coal mine safety accidents decreased. Industry structure, salary, security inputs, coal consumption and gross domestic product (GDP) played a significant role. The lever of impact of each factor on coal mine safety accidents was enhanced after interaction. The relationship between the factors entailed nonlinear enhanced interaction. The research findings not only contribute to a greater understanding of the macroscopic patterns of accident occurrence but also increase the awareness of the government and enterprises regarding the driving factors of coal mine safety accidents. Furthermore, such research holds crucial significance for precise prevention and control of coal mine safety accidents.

Greenhouse gas protection and control based upon the evolution of overburden fractures under coal mining: A review of methods, influencing factors, and techniques

In accordance with the Paris Agreement, many nations have set out a global framework to stabilise and prevent greenhouse gas (GHG) emissions to mitigate climate change. This study reviewed the literature on the GHG protection and control (GHGPC) impacts of overburden fractures in relation to coal mining. Different factors, such as geological conditions, mining conditions, and gas extraction methods, were compared because they influence GHGPC. Carbon emissions, coal mining safety, and environmental protection are crucial parameters. GHGPC can (1) improve the safety and efficiency of mining, (2) reduce GHG emissions, and (3) generate clean gas resources. During mining, cracks appear in overburden strata, causing GHG emissions. Various techniques, including scanning electron microscopy, nuclear magnetic resonance, two-dimensional and three-dimensional physical similarity simulations, permeability testing, and numerical simulations, have been adopted to determine the macroscopic and microscopic features of overburden fractures. Finally, the extraction method of GHG was optimised through field practice, namely, roadway, borehole, and pipe extraction, were analysed, and the comprehensive GHG extraction method was preferred to achieve superior management results. This paper reviewed studies that examine the development of fractures to elucidate the significance of GHGPC. Moreover, this article provides new insights into the achievement of the “dual carbon strategy” with particular reference to coal mining; it also summarises the holistic life cycle of gas management in terms of overburden activity, gas transport patterns, reservoir characteristics and typical technologies. It also summarises the holistic life span of GHGPC management in terms of overburden activity, GHG transport patterns, reservoir characteristics, and typical technologies. Finally, the authors discuss and conclude the article with proposed methods for future research directions.

Determination and analysis of macro-featured parameters based on kilogram-level coal spontaneous combustion experiment

The quantitative characterization of coal spontaneous combustion and the accurate determination of the dangerous degree are hot and difficult in the field of coal mine safety. In the paper, the experimental test analysis of Huangling No. 2 coal was carried out by a self-designed and built kilogram-level coal spontaneous combustion experimental device, and the macro featured parameters were obtained and analyzed, such as temperature field, index gas, weight, oxygen consumption rate and exothermic intensity, and spontaneous combustion limit parameters. The results show that the experimental spontaneous combustion period of Huangling No.2 coal is 34 days. When the coal temperature was below the critical temperature of 74.59 °C, the oxidation of coal samples was relatively slow, and the oxygen consumption and the production rate of gas products did not change much. After the coal temperature exceeded the critical temperature, the oxygen consumption rate and exothermic intensity increased faster and the oxidation accelerated. After reaching the dry cracking temperature of 104.93 °C, the oxidation degree accelerated sharply. CO starts to appear at the beginning of the experiment, and the increase of CO gas concentration during the heating process is exponential and can be used as an index gas. Under the temperature condition of 40 °C, the floating coal of 0.7 m needs 25.83% oxygen concentration, and the maximum air leakage intensity of the coal is negative, and no spontaneous combustion will occur. The test data and calculation results of the experiment are important for the prediction and forecast of natural fires and the determination of the risk of spontaneous combustion on site.

Research on Automatic Counting of Drill Pipes for Underground Gas Drainage in Coal Mines Based on YOLOv7-GFCA Model

Gas explosions threaten the safety of underground coal mining. Mining companies use drilling rigs to extract the gas to reduce its concentration. Drainage depth is a key indicator of gas drainage; accidents will be caused by going too deep. Since each drill pipe has the same length, the actual extraction depth is equivalent to the number of drill pipes multiplied by the length of a single drill pipe. Unnecessary labor is consumed and low precision is achieved by manual counting. Therefore, the drill pipe counting method of YOLOv7-GFCA target detection is proposed, and the counting is realized by detecting the movement trajectory of the drilling machine in the video. First, Lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Second, the (Fasternet-Coordinate-Attention) FCA network is fused into a feature fusion network, which improves the expression ability of the rig in complex backgrounds such as coal dust and strong light. Finally, Normalized Gaussian Wasserstein Distance (NWD) loss function is used to improve rig positioning accuracy. The experimental results show that the improved algorithm reaches 99.5%, the model parameters are reduced by 2.325 × 106, the weight file size is reduced by 17.8 M, and the detection speed reaches 80 frames per second. The movement trajectory of the drilling rig target can be accurately obtained by YOLOv7-GFCA, and the number of drill pipes can be obtained through coordinate signal filtering. The accuracy of drill pipe counting reaches 99.8%, thus verifying the feasibility and practicability of the method.