Mine Detection Research Articles

PDE modelling and switch strategy controller design for a flexible detection system with output constraint and actuator failure

A novel approach for modelling a mine detection robot system is presented in this paper using partial differential equations and Hamilton’s principle. The system comprises a rigid arm, a detecting sensor module, and a flexible string. The paper addresses the challenges of boundary deflection constraint and actuator fault. To overcome these challenges, a unique controller is developed using Barrier Lyapunov function and switching strategy. This controller enables the robot system to achieve the desired position and eliminate the vibration of flexible string. The superiority of the designed control law is demonstrated through simulation results. Furthermore, the asymptotic stability of the closed-loop flexible system is established by the use of extending LaSalle’s Invariance principle to an infinite dimensional system. This principle ensures that the system will eventually reach a stable state over time.

A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions.

Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.

Multi-Level Difference Network for Change Detection from Very High-Resolution Remote Sensing Images: A Case Study in Open-Pit Mines

Automatic change detection based on remote sensing is playing an increasingly important role in the national economy construction. To address the problem of limited change detection accuracy in existing single-level difference networks, this study proposes the Multi-level Difference Network (MDNet) for automatic change detection of ground targets from very high-resolution (VHR) remote sensing images. An early-difference network and a late-difference network are combined by MDNet to extract multi-level change features. The early-difference network can focus on change information throughout to reduce the spurious changes in the change detection results, and the late-difference network can provide deep features of a single image for reducing rough boundaries and scattered holes in the change detection results, thus improving the accuracy. However, not all high-level features extracted by MDNet contribute to the recognition of image differences, and the multi-level change features suffer from cross-channel heterogeneity. Stacking them directly on channels does not make effective use of change information, thus limiting the performance of MDNet. Therefore, the Multi-level Change Features Fusion Module (MCFFM) is proposed in this study for the effective fusion of multi-level change features. In the experiments, the publicly available open-pit mine change detection (OMCD) dataset was used first to achieve a change detection of open-pit mines over a large area, with an F1-score of 89.2%, increasing by 1.3% to 5.9% compared to the benchmark methods. Then, a self-made OMCD dataset was used to achieve an F1-score of 92.8% for the localized and fine-scale change detection in open-pit mines, which is an improvement of 0.7% to 5.4% compared to the benchmark methods. Finally, the Season-varying Change Detection Dataset is used to verify that the MDNet proposed can detect changes in other scenarios very well. The experimental results show that the proposed MDNet has significantly improved the performance of change detection on the three datasets compared with six advanced deep learning models, which will contribute to the development of change detection with VHR remote sensing images.

Development of a hybrid neural network model for mine detection by using ultrawideband radar data

The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is carried out by a Hilbert block and an oscillatory neural network, which are included in the structure of a hybrid neural network. The peculiarity of the obtained results, which allowed to solve the investigated problem, is the transformation of the signal frequency by the Hilbert block and the recognition of mines by the oscillatory neural network in the resonant mode. The three-layer MLP filter effectively filters out the unwanted component in the total signal reflected from the subsurface object, as the MSE (Mean Squared Error) of the MLP filter is 1·10-5. If the frequency of the Hilbert signal is equal to the natural frequency of oscillations of neurons then the recognition of signals with a small amplitude from subsurface objects is carried out by an oscillatory neural network based on the resonant amplitude, which is indicated by a small value of cross-entropy. The proposed model of a hybrid neural network provides amplification of useful signals due to resonance and has higher performance compared to existing models of artificial neural networks. The practical significance of the obtained results lies in their application in the field of automated neural network technologies for detection and recognition of subsurface objects of various nature based on reflected radar signals with an amplitude at the noise level

Synthesis of the system for detection of explosive objects on the base of an unmanned aerial vehicle

The article analyzes explosive objects of various types and provides their characteristics. Their unmasking feature is the material of the main part (casing), it is noted that high-explosive mines are characterized by the use of a plastic casing, for fragmentation mines – a metal casing, and the most difficult to detect are mines with a plastic casing. Methods of detecting explosive objects compared to those currently used in Ukraine are considered. Unfortunately, given the scale of mined areas, they are ineffective, so it is obviously necessary to develop more effective solutions for their detection and neutralization based on modern achievements of technical progress. Foreign countries have developed and use modern mobile robotic complexes for demining based on unmanned aerial vehicles with various types of sensors installed on them. It is expedient to create a universal detection system that can be deployed on any helicopter-type unmanned aerial vehicle (quadcopter, multicopter), on which several detection sensors are installed at the same time for the purpose of reconnaissance of the mine situation, detection of mines and their remote destruction. The article proposes the synthesis of a system for detecting explosive objects based on an unmanned aerial vehicle with a thermal imager installed on it, in combination with a metal detector and a manipulator with explosives for remote demining. The parameters of detection of explosive objects, the type of sensor sensors and the main functions of the system are considered. The algorithm of actions of the operator of the system for detecting explosive objects is proposed.

Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine.

Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine detection. Using the UAV-borne multispectral cruise platform, we establish a multispectral dataset of scatterable mines, with mine-spreading areas of the ground vegetation considered. In order to achieve the robust detection of occluded landmines, first, we employ an active learning strategy to refine the labeling of the multispectral dataset. Then, we propose an image fusion architecture driven by detection, in which we use YOLOv5 for the detection part, to improve the detection performance instructively while enhancing the quality of the fused image. Specifically, a simple and lightweight fusion network is designed to sufficiently aggregate texture details and semantic information of the source images and obtain a higher fusion speed. Moreover, we leverage detection loss as well as a joint-training algorithm to allow the semantic information to dynamically flow back into the fusion network. Extensive qualitative and quantitative experiments demonstrate that the detection-driven fusion (DDF) that we propose can effectively increase the recall rate, especially for occluded landmines, and verify the feasibility of multispectral data through reasonable processing.

Approaches to complete coverage path planning using neural network and grid division

Since the goal of using complete coverage path planning is to generate a continuous and uninterrupted path that covers an area of interest while avoiding obstacles, its use could be extremely vital in today's field of robotics. Not only could it help our daily lives like lawnmowers, window cleaners, and painter robots, but it could also solve some dangerous or complex but vital problems for human beings; For example, mine detection, vacuum cleaning, and photogrammetry. This paper proposes two paths of successful approaches to Complete Coverage Path Planning: Neural Network and Grid division. After detailed data comparisons, both proved to have been efficient and successful, respectively. In addition, both plans' field applications would be placed at the end.

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering

Underground obstacle detection is the premise of unmanned driving. Aiming at the problem of obstacle detection in coal mines using a millimeter-wave radar, this paper proposes a filtering algorithm for invalid targets in roadways. Firstly, the target information output by millimeter-wave radar is analyzed, and the obstacle information of the underground roadway is extracted. Then, the filter algorithm is used to filter the resolved empty targets, false targets and non-potentially dangerous targets. The empty target whose target distance is zero in radar data is filtered directly. The pseudo targets generated by the radar performance or the instability of the echo signal are filtered through the radar-effective target life cycle method. The non-threatening targets beyond the horizontal range threshold and longitudinal range threshold are filtered directly. The experimental results show that the average filtering rate of the algorithm is more than 87.82% in the static state. When the speed is 5 km/h, the average filter rate is 87.70% for smooth driving and 86.83% for uneven road surfaces. When the speed is 7 km/h, the smooth driving rate is 87.54%, and the uneven road surface is 86.56%. When the speed is 10 km/h, the smooth driving rate is 86.50%, and the uneven road surface is 86.44%. Although the filter rate decreases with the increase of speed or vehicle vibration in the running state, the average filter rate of the proposed algorithm can reach more than 86% under all conditions.

Land Mine Detection Robotic Vehicle with GPS and GSM Tracking System

Abstract: This paper presents a new system for landmine detection that combines advanced technology with ease of use. The proposed system is a robot vehicle equipped with a metal detector, GPS, GSM, and a microcontroller that enables it to detect and locate landmines in real-time. The robot vehicle is designed to be controlled remotely by a smartphone app, making it suitable for use in hazardous areas without risking human life. The GPS and GSM capabilities of the robot vehicle allow it to transmit its location and detected data to a control center, making it easy for operators to track and analyze the data. The proposed system is a significant advancement in landmine detection technology, offering a safe, efficient, and reliable solution for detecting landmines in hazardous areas.

Landmine Detector Robotic Vehicle

Nowadays, in countries such as Afghanistan and Iraq, land mines are posing a major danger to the lives of civilians. These mines are explosive devices that are designed to disable or destroy enemy targets and are typically hidden beneath the surface of the ground. Most land mines are placed just below the ground and are triggered by pressure or trip-wires. They typically contain a number of metallic components, which can be used to detect them. The mines that are set up during times of war may remain undetected for extended periods. The term "Metal Detector" implies that it can be done from a distance, allowing for a safer approach to locating landmines. Using a mine detection robot, soldiers can traverse mine-ridden areas without entering them, providing a secure and efficient way of landmine detection. Other methods, such as radar bullets, biological methods, and mechanical methods, are much risky and put soldiers' lives at risk. The Landmine Detection Robot, which utilizes both IoT and GPS technology, is a much safer option.

Humanitarian demining for the clearance of landmine-affected areas

Humanitarian demining is the process of removing landmines from contaminated areas. Behind this arduous task lies a high risk of collateral damage to deminers and their equipment. The intrinsic operational cost often forces agents to abandon the task before its completion, making such areas a source of risk, especially for the civilian population and biodiversity. Most of the research to date has revolved around the technological development of devices capable of effectively detecting mines at a selected point within an area, while the search strategy itself to decide where to explore and which route to follow has been understudied. This paper proposes a decision support model (DSM) that produces highly safe and cost-effective mine detection search plans. Since the computational time of the solution increases along with the size of the area explored, exact linear methods are proposed for small and medium instances of the problem, whereas a genetic algorithm (GA) is proposed for large instances. Results show that the GA approach is capable of delivering solutions close to the optimum.

Humanitarian Demining Serial-Tracked Robot: Design and Dynamic Modeling

The paper proposes an original mechanical structure of a serial-tracked robot, subject of national invention patent number RO132301, B1/2021, destinated for humanitarian demining operations: anti-personnel mine detection by using a detection device mounted on the bottom’s tracked platform, demining and clearing the land of exploded mines using a TRTTR robot structure. The dynamic model of the robot structure is determined and numerically validated. A novel approach based on the Lagrange formalism and mechanical design equations has been used in the calculus and selection of robot driving motors. The obtained results for robot translation modules are presented and analyzed.

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitive and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

A Comprehensive Analysis of Security-Based Schemes in Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are comprised of sensor nodes that are deployed under the water having limited battery power and other limited resources. Applications of UWSNs include monitoring the quality of the water, mine detection, environment monitoring, military surveillance, disaster prediction, and underwater navigation. UWSNs are more vulnerable to security attacks as compared to their counterparts such as wireless sensor networks (WSNs). The possible attacks in UWSNs can abrupt the operation of entire network. This research work presents the analysis of relevant research done on security-based schemes in UWSNs. The security-based schemes are categorized into five sub-categories. Each technique in each category is analyzed in detail. The major contribution in each security-based scheme along with technique used, possible future research issues and implementation tool are discussed in detail. The open research issues and future trends identified and presented in this research can be further explored by the research community.

The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines.

Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.

FEGNet: A feature enhancement and guided network for infrared object detection in underground mines

Object detection plays an important role in underground intelligent vehicles and intelligent transportation systems. Due to the uneven light in underground mining scenarios, infrared cameras are one of the typical onboard sensors for environmental perception. Although object detection has been studied for decades, it still confronts the challenge of detecting infrared objects in underground mines. The contributing factors include weak and small objects in infrared images and similar environments in mining scenarios. In this paper, a Feature Enhancement and Guided Network (FEGNet) is proposed to address these problems. Based on the characteristics of infrared images, the feature enhancement module (FEM) preserves the image details from global and local perspectives to improve the discrimination of weak and small objects. To tackle the problem of overfitting caused by similar environments, a receptive-field-guided (RFG) backbone is proposed to learn multi-scale context and spatial information. The experimental results on the underground mining (UM) dataset demonstrate that the mAP of the proposed FEGNet achieves 86.1%, which is 4.6% higher than the state-of-the-art CNN-based network YOLOv7.

Experimental Study of Sandwich Strength Materials Made from Banana Fiber, as Advance Materials of the Unmanned Mine Sweeper (UMS) Hull

Mine countermeasures (MCM) is a sea operation that is very risky for personnel and material losses. The development of mine technology with the discovery of modern digital mines technology currently makes mine warfare strategies change, namely by relying on the unmanned minesweeper (UMS) as a mine detector, to mitigate the impacts and risks of sea mines on naval operations. UMS vessel size is 4.8m LoA, speed 2.57m/s and beam 2.24m. The main requirements for UMS are low magnetic, low vibration, and low hydrostatic. In this study, the author will make a sandwich material made from banana fibre as an Unmanned Mine Sweeper gastric material. The choice of banana fibre is because in addition to low magnetic, its availability is very abundant in Indonesia and the price is relatively cheaper than kevlar fibre. This study uses the technique of manufacturing vacuum assessed resin infusion (VARI). Then the strength test will be carried out with a tensile test, impact test and hardness test using the ASTM standard and compared with wood and fibre materials. The test results show that abaca banana fibre is capable of being used as an unmanned minesweeper hull material.

Research on Metal Target Classification Method Based on Multi-Frequency Electromagnetic Induction

The traditional single-frequency metal mine detector is limited by the single-frequency component, which is difficult to meet the requirements of high-accuracy detection of multiple types of mine targets. The modulation square wave is used to transmit the multi-frequency signal based on the multi-frequency electromagnetic induction technology. The phase-locked amplifier is used to extract the signal feature of each frequency component. The naive bayesian algorithm is used to classify metal targets according to the extracted feature vectors. This method can effectively improve target classification accuracy compared with single-frequency metal mine detectors.

Land mines detection, mapping and clearance using Quadcopter in Yemen: A perspective study

People who live or work in areas where there have been armed conflicts may face the threat of landmines and explosive hazards, such as unexploded or abandoned ordnance, abandoned military vehicles, and equipment. Therefore, this is a tremendous obstacle to development and construction in such areas, which requires awareness of the threat and appropriate preventive action. This paper aims to facilitate the detection and elimination of planted landmines in Yemen. Two stages are proposed to be implemented in this study for the demining techniques: detection and clearance phases. The first phase uses a Quadcopter with detecting sensors to locate and map the mines' spot, while the second stage uses a Quadcopter to unload a mass on that determined location from a certain height. By employing these two stages, it is expected to quickly and accurately detect the locations of landmines. That will reduce risk, protect human lives, and provide a more efficient and safe way to sweep minefield workers using sample, small, and easy control equipment.