Mine Detection Research Articles

Miner pose detection algorithm based on improved YOLOv7 in the coal mine

The unsafe behavior of workers in underground mines is a major cause of accidents. Recently, deep learning technology has become more and more widely used in the computer field and has achieved remarkable results, providing new opportunities for the research of miner behavior recognition algorithms. To address the current problem of large computation volume and low detection efficiency of multi-person posture detection in underground mines, we propose an improved algorithm that introduces the GSConv convolution module into the YOLOv7-Pose model and replaces the ELEN-W module with the GSELEN module to accelerate model convergence. We also introduce a group-convolution reconstruction SPPCSPC module to reduce parameter load and calculation amount and reduce the original 17 joint points to 14 joints to further realize a lightweight model. The experimental results showed that the improved YOLOv7-Pose model in this study decreased to 135.7 MB, by about 18.8%, while maintaining model accuracy. Therefore, this improvement can achieve a lightweight algorithm and improve real-time posture detection of construction workers in underground mines while ensuring model accuracy.

Sensing Using Terahertz Radiation

Terahertz (THz) sensing technology enables 6G communication, detection of biological and chemical hazardous agents, cancer detection, monitoring of industrial processes and products, and detection of mines and explosives. THz sensors support security in buildings, airports, and other public spaces. They found important applications in radioastronomy and space research and, more recently, in Artificial Intelligence-driven THz sensing of MMICs and VLSI. Exploding demand for data transfers will require using the 300 GHz band after 2028 or even before and will make the deployment of THz sensing electronics inevitable. This paper discusses the new physics of THz sensing and THz sensing devices. It also reviews the THz sensing market, and key THz sensor companies.

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s.

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Machine learning‐assisted wearable sensor array for comprehensive ammonia and nitrogen dioxide detection in wide relative humidity range

AbstractThe fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers, and a variety of gas sensors have been simultaneously developed. However, these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture, resulting in less accuracy in the analysis of gas compositions in real mining conditions. To address these challenges, we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH3 and NO2 gas detections under real mining conditions. A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions. Further introduction of backpropagation neural network (BP‐NN) and partial least squares (PLS) algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture, realizing over 99% theoretical prediction level on NH3 and NO2 concentrations within a wide relative humidity range, showing great promise in real mining detection. As proof of concept, a wireless wearable bracelet, integrated with sensing arrays and machine‐learning algorithms, is developed for wireless real‐time warning of hazardous gases in mines under different humidity conditions.image

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

THEORETICAL APPROACHES TO THE SELECTION OF DETECTION METHODS OF EXPLOSIVE DEVICES IN ENVIRONMENTS THAT CONCEAL THEM, ADVANTAGES AND DISADVANTAGES

The results of the analysis of trends in the development of armed struggle indicate the ever-increasing role of the use of mine weapons. At the same time, there is a significant imbalance between the development of mine weapons and detection means, which in turn causes a number of problematic issues in demining. It is noted that due to the aggression of the russian federation, Ukraine has become the most mined country in the world. In addition to the problem of demining the contaminated territory with explosive objects, the issue of reconnaissance of the area for mines and making passages in enemy minefields has become acute.
 The paper analyzes the demining process, identifies the factors that influence it, and presents the main demasking factors of explosive objects, as well as the performance of terrain demining tasks. Taking into account these features, theoretical approaches to the detection and identification of explosive objects based on various physical principles are considered. An analysis of the use of the main methods of detection and identification of explosive objects is carried out, their advantages and disadvantages are determined.
 Based on the analysis of theoretical approaches, a generalized characterization of the main methods of detecting explosive objects is given. It is noted that, taking into account the experience of performing demining tasks in the war of the russian federation against Ukraine, the main methods of searching for explosive objects are contact, mechanized, induction, optical, thermal and in some cases biophysical. Today, the use of advanced explosive detection methods is limited by the need to solve technical problems to reduce the number of false signals. The paper notes that in order to increase the efficiency of the process of detecting explosive objects and ensure proper safety, it is advisable to combine detection methods, as well as combine them with remotely controlled (robotic) means, as well as UAVs.

Pelatihan Pengoperasian Alat Magnetometer untuk Identifikasi Ranjau dengan SATRAN ARMADA II TNI-AL di Selat Madura

Magnetometer Operation Training for Mine Identification with G-882 Magnetometer equipment and the use of the SonarWiz software application in the Madura Strait is a training initiative that aims to increase the knowledge, skills and capabilities of Mine Ship Unit (STRAN) personnel of the TNI-AL ARMADA II in operating the tool. magnetometer to detect and identify mines in the waters of the Madura Strait. In this training, participants will be given an in-depth understanding of the working principle of the magnetometer, correct operating techniques, and magnetic data analysis methods to identify anomalies that indicate the presence of mines. In addition, participants will also be trained in operating the G-882 Magnetometer and using the Sonar Wiz software application, this support system that strengthens mine detection and identification capabilities. The proposed solution is through intensive training which includes theory sessions, field practice, processing of magnetometer data and which bombs can be identified and simulation of real situations for the 0.5-5 (nT) classification including 16m depth bombs and 1-5 (nT) including bombs. with a depth of 30m. Participants will be actively involved in practicing the operation of the magnetometer device, data analysis, and interpretation of results to increase their understanding of the characteristics of mines in the Madura Strait waters. It is hoped that this training will produce positive outcomes, such as increasing the knowledge, skills and confidence of Mine Ship Unit (STRAN) II TNI-AL personnel in operating the magnetometer device for mine identification. In addition, it is hoped that there will also be close cooperation between Mine Ship Unit (STRAN) personnel from the Indonesian Navy and the people of the coastal areas in supporting the task of securing the waters of the Madura Strait.

Identification of potential marker bacteria with metagenome data in boron mine detection

Mining exploration programmes are being expensive and risky due to the limitations of conventional prospecting methods in identifying desired mineralisation. Worldwide, mining exploration programmes have a success rate of approximately 0.7%. Bio-geochemical methods, a sub-branch of geochemical prospecting, are used to identify potential mineralisation areas by studying the distribution of plants containing certain metals. Turkey's leading position in the world in terms of boron reserves increases the strategic importance of boron. On the other hand, metagenome analysis is an important tool in scientific research for detecting microorganisms. Our study aims to develop a cost-effective and efficient biological alternative method for detecting boron mines. Metagenome analysis output showed correlation with the presence of Calothrix sp. NIES-3974, Snodgrassella alvi, Xanthomonas citri, X. citri pv. Fuscans, Pseudomonas viciae, and Aphanothecaceae bacteria and detecting of the potential boron traces in soil. Modelling studies were also conducted on aquaporin z protein, related to the boron uptake, that can be used for utilising from boron detection.

Statistical detection of selfish mining in proof-of-work blockchain systems

The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner’s behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner’s successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.

Underwater small target detection based on dynamic convolution and attention mechanism

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

BitTrace

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

A Statistical Evaluation of the Connection between Underwater Optical and Acoustic Images

The use of Synthetic Aperture Sonar (SAS) in autonomous underwater vehicle (AUV) surveys has found applications in archaeological searches, underwater mine detection and wildlife monitoring. However, the easy confusability of natural objects with the target object leads to high false positive rates. To improve detection, the combination of SAS and optical images has recently attracted attention. While SAS data provides a large-scale survey, optical information can help contextualize it. This combination creates the need to match multimodal, optical–acoustic image pairs. The two images are not aligned, and are taken from different angles of view and at different times. As a result, challenges such as the different resolution, scaling and posture of the two sensors need to be overcome. In this research, motivated by the information gain when using both modalities, we turn to statistical exploration for feature analysis to investigate the relationship between the two modalities. In particular, we propose an entropic method for recognizing matching multimodal images of the same object and investigate the probabilistic dependency between the images of the two modalities based on their conditional probabilities. The results on a real dataset of SAS and optical images of the same and different objects on the seafloor confirm our assumption that the conditional probability of SAS images is different from the marginal probability given an optical image, and show a favorable trade-off between detection and false alarm rate that is higher than current benchmarks. For reproducibility, we share our database.

A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud

In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.

Diseño y Simulación de una Antena Fractal Log-Periódica para la Detección de Minas de Plástico

A fractal log periodic antenna for a Ground Penetrating Radar (GPR) is designed and simulated to evaluate the detection of plastic mines. The Brewster pseudo-angle concept is considered in the simulation to force the reflection component to a minimum value. The methodology focuses on the characterization and modeling of the transmission media and the plastic mine, in addition, the presence of the standing wave in the system is simulated to evaluate the return signal in the receiving antenna of the GPR. As a result, the multiband characteristic of the log-periodic fractal antenna and the signal-to-noise ratio (SNR) of 10,17 dB obtained for the resonance frequency of 1,35 GHz stand out. Likewise, the pseudo-Brewster angle of 70,47° minimizes the reflection of the incident electromagnetic wave in the transition between two dielectric media, with losses and dispersive in frequency (free space-ground).

Operational data for the risk management of victim operated explosive devices in humanitarian mine action: A Practitioner's perspective

Since Mine Action's inception at the end of the 1980s, operators have collected limited data on the Victim Operated Explosive Devices (VOEDs) they clear. This includes not only data on the explosive ordnance itself but data on how they were found, where they were found and how they were processed and ultimately destroyed. In a context where detection of mines, boobytraps and certain Victim Operated Improvised Explosive Devices (VOIEDs) is an ongoing humanitarian and military challenge, significantly expanded operational data collection provides an achievable way to facilitate enhanced operational risk management. Risk decisions inherent in the clearance of VOEDs are better if made on the basis of extensive operational data. In the absence of a technological solution to detect and positively discriminate VOEDs from false positive indications, the collection of operational data offers the best prospect for “managing” if not “solving” the problem.

A Systematic Review of the Sarcasm Detection in the Twitter Dataset

Text is the most significant contributor to data generated on the Internet. Understanding a person's opinion is an essential part of natural language processing. However, people's views can be skewed and inaccurate if people use sarcasm when they post status updates, comment on blogs, and review products and movies. Sarcasm detection has gained an important role in social networking platforms because it can impact many applications such as sentimental analysis, opinion mining, and stance detection. Twitter is rapidly growing in volume, and its analysis presents significant challenges in detecting sarcasm. Our research work focuses on various methodologies available for detection of sarcasm. Various papers from recent years were collected and review was carried out. This paper discusses the literature on sarcasm detection under the category of datasets, in different pre-processing, feature extraction, feature selection, classification algorithms, and performance measures. This paper discusses the literature on sarcasm detection under the category of datasets, in different pre-processing, feature extraction, feature selection, classification algorithms, and performance measures. This work explores existing approaches, challenges, and future scopes for sarcasm detection in the Twitter dataset. This review bringsto light the analysis ofsarcasm identification in Twitter data and is intended to serve as a resource for researchers and practitioners interested in sarcasm detection and text classification.

Influence node analysis based on neighborhood influence vote rank method in social network

Social Networks are used for various purposes like advertising, product launches, sentiment analysis, opinion mining, and event detection etc. Terrorist targets social network users to spread the terrorism. Influence analysis is used in social networks to find the influence of users and the impact of the messages, mainly for advertising. In this research, the Neighborhood Influence – Vote Rank (NI-VR) method is proposed to analyze the terrorism and social network datasets temporally to find the influence node in Social networks. The Global Terrorism Dataset (GTD) was used to analyze the terrorism activity and temporal analysis on Social Network data to find the influence node. The Neighborhood node influence is measured and considered in the Social Network data to effectively find the influence node. The nodes’ vote score and vote ability were measured to rank the nodes based on influence. The neighborhood influence is measured to update the vote score and vote ability based on influence value. The neighborhood influence method is applied to rank the node has the advantage of analyzing the probability of affected nodes and recover nodes that help to effectively find the influence nodes. The outcomes illustrate that the proposed NI-VR achieved a maximum spread influence of 843 and the existing Greedy method has a higher spread influence of 840 in influence node analysis.

Research on Coal Volume Detection and Energy-Saving Optimization Intelligent Control Method of Belt Conveyor Based on Laser and Binocular Visual Fusion

Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang’s calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system’s performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt’s operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.

High-Resolution Hybrid-Dimensional Inversion of Transient Electromagnetic Data for Water Hazard Detection in Coal Mines

High-Resolution Hybrid-Dimensional Inversion of Transient Electromagnetic Data for Water Hazard Detection in Coal Mines