Unexploded Ordnance Detection Research Articles

Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System.

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10-10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.

Novel approaches of borehole‐GPR data processing and visualization – application for unexploded ordnance detection

AbstractBorehole ground‐penetrating radar (BGPR) measurements allow for the detection of objects and structures in the subsurface and are often applied to the detection of unexploded ordnance (UXO). If omnidirectional borehole antennas in reflection mode are used for the measurement, the localization of UXO is only possible if the data from a multitude of boreholes are analysed. Data analysis is usually still done by manual picking of reflections. We propose novel approaches to process and visualize data from BGPR measurements in a more advanced and appealing manner. Therein, the reflected energy recorded in the radargrams is projected back to all potential reflection points in the three‐dimensional space around the boreholes. If the projection direction is considered, we obtain a vectorized energy projection image. Superposition of projected energy yields an easy‐to‐grasp indicator of possible locations of UXO and of regions of interest that ought to be investigated in more detail. These approaches have been applied to synthetic data and to data measured on a test site with buried UXO. The results show that energy projection is a useful tool for BGPR data visualization, although the result is dependent on data pre‐processing. The proposed methods provide novel representations of BGPR data based on an objective algorithm which will at least complement the conventional methods.

Probing with Each Step: How a Walking Crab-like Robot Classifies Buried Cylinders in Sand with Hall-Effect Sensors.

Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in load bearing compliant segments. The magnetometers are sensitive to magnetic objects in close proximity to the legs and their movement relative to embedded magnets, allowing for both proximity and force-related feedback in dynamically obtained measurements. A dataset of three-axis measurements is collected as the robot steps near and over different UXOs and UXO-like objects, and a convolutional neural network is trained on time domain inputs and evaluated by 5-fold cross validation. Additionally, we propose a novel method for interpreting the importance of measurements in the time domain for the trained classifier. The results demonstrate the potential for accurate and efficient UXO and non-UXO discrimination in the field.

Аналіз методів та засобів комп’ютерного зору для мобільних систем пошуку вибухонебезпечних предметів

The detection and removal of unexploded ordnance and landmines are vital for ensuring civil-ian safety, enabling the repurposing of affected land, and supporting post-conflict recovery ef-forts. Robotization plays a pivotal role in addressing the hazardous and labor-intensive nature of demining operations. The purpose of this paper is to review prominent computer vision tech-niques, particularly object detection, and analyze their applications in the specialized domain of explosive ordnance detection. An extensive review of the literature was conducted to explore the utilization of computer vision in the field of explosive ordnance detection. The study in-volved a comparative analysis of diverse imaging sensors employed for data capture. Special at-tention was given to reviewing sources that elaborate on different methods for detecting objects within the field of computer vision. Various approaches to object detection were thoroughly examined and compared. The research extensively examined the metrics and datasets used to evaluate different approaches for object detection. Possibilities of applying computer vision methods to detect explosive ordnance under the limitations of mobile platforms were studied. Directions of future research are formulated.

Intelligent, Automated, Rapid, and Safe Landmine, Improvised Explosive Device and Unexploded Ordnance Detection Using Maggy

Intelligent, Automated, Rapid, and Safe Landmine, Improvised Explosive Device and Unexploded Ordnance Detection Using Maggy

Preliminary results of UAV magnetic surveys for unexploded ordnance de-tection in Ukraine: effectiveness and challenges

Over the past decade, the development of unmanned aerial technologies has allowed them to be considered a tool for conducting remote Earth sensing research. The use of such systems is primarily associated with solving geological and archaeological tasks. However, the armed aggression by the Russian Federation contaminated Ukrainian territory at an unprecedented scale, including unexploded ordnance (UXO). It is necessary to check unmanned aerial vehicles (UAVs) can be used to address this issue. An efficient solution can significantly expedite the process of surveying areas for the presence of UXO, minimizing risks to lives and health.
 The research was conducted at test sites using full-scale models as targets, as well as disarmed mines and other ordnance. Additionally, a magnetic survey was carried out in areas directly affected by combat operations. Overall, within the scope of this work, approximately 30 hectares of territory were surveyed under various shooting modes and conditions. As expected, a high detection rate is characteristic of target objects with a high ferrous metal content. Unfortunately, the widespread use of plastic ordnance with low iron content and the high density of craters blasts create non-target magnetic anomalies, affecting the overall detection. However, with proper planning and shooting modes, the task of detection is partially addressed. Our preliminary results allow us to draw initial conclusions about the effectiveness of the complex and identify issues that require resolution.

Creation of the first UXO detection test site in Slovakia at the Rohožník military training range

In the development of geophysical methods for unexploded ordnance detection, a very important role is played by UXO test sites, where known ordnance and other explosive/nonexplosive items are buried in the ground at defined positions. At such sites, various detection methods can be compared, developed and tested. Based on a cooperation between the Department of Applied Geophysics (Comenius University), Institute of Forensic Science (Slovak Ministry of Interior) and Rohožník military training range a project was performed, which was focused on the creation of the first UXO detection test site in Slovakia. It was restricted to one type of ordnance – inert tank projectiles with diameter of 100 mm. These were buried in the ground at different depths and with different orientations. Data acquisition mapping the test site was performed from the ground surface, using total field intensity magnetometers, vertical difference magnetometers and ground penetrating radar (GPR). Data acquired from all methods were processed, interpreted and archived for future reference. Most of the items were readily detected by each of the detection sensors used but the deepest items at the least favourable orientation exceeded the detection limit of the magnetometers trialled with total field performing better than the vertical gradiometers. For the application of GPR, this site was found to have favourable soil conductivity conditions permitting even the deepest items to be detected when favourably oriented. Vertical orientation presenting the smallest reflective cross-section was least favourable with some shallow items escaping detection. The most accurate depth estimations for detectable items were obtained from GPR data presented as 2D non-migrated vertical sections. Geophysical data sets acquired at seeded test sites such as the one now established at the Rohožník military training range can provide important base-line data for comparison from which development of new detection and data interpretation technologies can be evaluated.

Data-model comparison of elastic target scattering for a shallow water sonar system for buried UXO detection

Successful buried unexploded ordnance (UXO) detection in shallow water (<3 m) has been demonstrated using a sonar system deployed from a shallow-draft surface vessel. Data was collected over several years and during different seasons in a freshwater reservoir near Howard, PA, USA. The target test field contains a variety of science (e.g., sphere, cylinder) and other objects. Previously, a point-based sonar scattering model that simulates calibrated time-series sonar data due to sediment surface and volume scattering effects was shown to provide simulated data of the environment. Recently, object scattering models have been added to the simulation. The results of a model-data comparison of objects in the environment will be presented. Realistic simulation results containing objects will increase understanding of detection capability in different geometries (e.g., depth) and environments (e.g., sediments, layering).

UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning

A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People’s Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 × 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants’ detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model’s complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.

Underwater UXO detection using magnetometry on hovering AUVs

AbstractThe EU‐funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows, http://www.basta-munition.eu) aimed at improving underwater unexploded ordnance (UXO) detection approaches and advancing data acquisition techniques. One aspect of the project was performing autonomous underwater vehicle (AUV)‐based magnetic measurements. In this paper, we present the first results of integrating three submersible fluxgate magnetometers to a Girona 500 AUV in the context of underwater UXO detection. The hovering capabilities of these AUVs allow them to maintain a fixed position or to precisely navigate at very low velocities and altitudes. The magnetic sensors are rigidly attached to the nose of the AUV at a lateral distance of 2 m and are arranged in the shape of a vertical triangle, thereby allowing for the calculation of three spatial magnetic gradients. A series of surveys was performed when visiting several munitions dumpsites in the German Baltic Sea. Furthermore, we successfully conducted a test survey with surrogate objects of known magnetic moments in a naval port basin in Kiel, Germany. With a noise floor of approximately 2 nT, the system is capable of reliably detecting munitions similar in size to 81 mm shells from altitudes of 1 m above the seafloor. For ground‐truthing purposes and for a concluding confirmation or rejection of a UXO suspicion, the AUV is equipped with a high‐resolution camera system. This newly developed system aims at improving the industry standard's technical potentials of autonomously discriminating between hazardous UXO and anthropogenic debris or rocks and therefore reducing the number of target points before underwater UXO clearance campaigns.

Overhauser Sensor Array Based 3-D Magnetic Gradiometer for the Detection of Shallow Subsurface Unexploded Ordnance

The information on the high-precision and multi-directional magnetic gradient is highly relevant to detecting and locating shallow subsurface unexploded ordnance. In this paper, the design of a three-dimensional (3D) Overhauser magnetic gradiometer is presented. Taking the sensor array interferences and working efficiency into account, a time slice overlapping based operating time sequence is devised. A precision narrow-band tuning capacitance network based on a cascaded subdivision strategy is constructed to improve the signal-to-noise ratio (SNR) of free induction decay (FID) signals. Additionally, a frequency measuring approach combining multi-phase clock and multi-channel is developed to suppress counting errors and improve the frequency measuring accuracy of FID signals. The 3D Overhauser magnetic gradiometer’s effectiveness was validated through extensive laboratory and field experiments. The laboratory test results show that the magnetic measuring performance of each sensor from the Overhauser magnetic sensor array is consistent. The proposed 3D Overhauser magnetic gradiometer is able to measure magnetic fields from 20 μT to 100 μT with an indicating error of less than 0.1 nT. The field exploration demonstrates the applicability of the gradiometer for shallow subsurface unexploded ordnance detection within the urban complex electromagnetic surroundings. Overall, this study can provide support for the accurate detection and localization of shallow subsurface unexploded ordnance under strong interference surroundings.

Automatic subsurface unexploded ordnance detection using a wideband full-polarization ground penetrating radar and entropy-based polarimetric signatures

An automatic buried unexploded ordnance (UXO) classification methodology based on the H-α entropy-based polarimetric features extracted from wideband (50–600 MHz) fully polarimetric ground penetrating radar (GPR) data is presented. This method first established a background H-α feature of the whole test site for determining the classification boundary of random and isotropic scatters. Then UXO-like elongated targets are detected by the presence of sufficient H-α feature appearing in the linear scattering classification zone. The detail processing and detection procedure will be discussed. The performance of the proposed UXO classifier will be evaluated using receiver operating characteristic (ROC) curve based actual field data collected at the US government Fort Ord UXO test site setup for evaluating different UXO detection technologies.

Aerial multi-spectral AI-based detection system for unexploded ordnance

Unexploded ordnance (UXO) poses a threat to soldiers operating in mission areas, but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard. Recent technological advancements in artificial intelligence (AI) and small unmanned aerial systems (sUAS) present an opportunity to explore a novel concept for UXO detection. The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral (MS) sensor on sUAS. This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single (visible) spectrum (SS) or MS digital electro-optical (EO) sensor. Specifically, it describes the design of the Deep Learning Convolutional Neural Network for UXO detection, the development of an AI-based algorithm for reliable UXO detection, and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.

Feasibility of UXO detection via pulsed neutron–neutron logging

The technique of pulsed neutron–neutron logging (PNNL) is applied in geosciences and petrol chemistry in order to measure hydrogen sources in downhole oil exploration. 14.1MeV neutrons from a deuterium–tritium fusion source provide also the capabilities for a time-of-flight based approach to identify the fillers of unexploded ordnances (UXO) by characterizing their hydrogen, nitrogen and oxygen contents. Using the Monte-Carlo tool URANOS we find that soil moisture is the key variable for the penetration depth of neutrons into the ground. Later detection windows after the initial pulse are to be preferred over earlier thresholds. Taking exemplarily a 100lb and a 1000lb bomb the range in a typical soil configuration is found to lie between 10cm and 50cm at a soil bulk density of 1.4g/cm3. Yet, other objects like a steel bar can cause similar signatures of signal dampening.

Commercial operation of a SQUID-based airborne magnetic gradiometer

Gradient measurement of the magnetic field vector, especially full-tensor magnetic gradiometers (FTMGs), provides various advantages over gradient components derived from measurements of the total magnetic intensity (TMI). These advantages include higher spatial resolution, directional information, and thus more detailed anomaly delineation and a significantly better-constrained solution space for magnetic inversion and interpretation. However, the airborne application of FTMG instruments requires exceptionally high sensor resolution and low levels of motion noise to maximize these advantages and to achieve high exploration depth. Superconducting quantum interference detectors (SQUIDs) are an effective option for FTMG sensors, now available commercially in the system discussed herein. This SQUID sensor system comprises intrinsic planar-type gradiometers that produce data with sufficiently low noise for use on an airborne platform. The evolution and advancement of the system and its predecessor over the past two decades has produced a robust commercial system that produces high-quality full-tensor data sets from a helicopter-towed-bird implementation. Because the SQUID sensors measure directionally sensitive data, the processing of the acquired data is significantly more challenging than for TMI sensors. Noise induced by the motion of the bird during flight, especially rotational noise, must be monitored and compensated. The introduction of a more robust and aerodynamic bird has significantly reduced the noise of the system. This noise reduction translates into greater sensitivity and accuracy and, thus, heightened confidence in the use of the survey data sets. While much of the early use of the system has been in diamondiferous kimberlite exploration, the system has successfully flown surveys in mineral exploration for a variety of targets, including gold, nickel, and iron ore. These data sets provide greater confidence in the geologic interpretation across the survey areas. Other applications for FTMG surveying include infrastructure mapping, unexploded ordnance detection, and compensation of electromagnetic data sets in marine environments.

Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak

The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) and optical cameras. AUVs can image large areas of the seafloor in high resolution, motivating an automated approach to UXO detection. Modern methods commonly use supervised machine learning which requires labelled examples from which to learn. This work investigates the often-overlooked labelling process and resulting dataset using an example historic UXO dumpsite at Skagerrak. A counterintuitive finding of this work is that optical images cannot be relied on for ground truth as a significant number of UXOs visible in SAS images are not in optical images, presumed buried. Given the lack of ground truth, we use an ordinal labelling scheme to incorporate a measure of labeller uncertainty. We validate this labelling regime by quantifying label accuracy compared to optical labels with high confidence. Using this approach, we explore different taxonomies and conclude that grouping objects into shells, bombs, debris, and natural gave the best trade-off between accuracy and discrimination.

High-Speed Magnetic Surveying for Unexploded Ordnance Using UAV Systems

Using Uncrewed Aerial vehicles (UAVs) to rapidly scan areas for potential unexploded ordnance (UXO) can provide an efficiency increase while minimizing detonation risks. We present a complete overview of how such mappings can be performed using scalar magnetometers, including initial sensor testing, time stamping validation, data positioning, noise removal, and source model inversion. A test survey was performed across disarmed UXO targets, during which three scalar magnetometers were towed in an airframe (“bird”) 10 m below a small (<25 kg) high speed (∼10 m/s) UAV to avoid magnetic disturbances from the UAV itself. Data were collected across ∼58 min of flight, with each sensor traversing ∼31.7 km to acquire dense data coverage across a 600 m × 100 m area. By using three individual magnetometers in the bird, UXO detection results across single-sensor data and several different multi-sensor configurations can be compared. The data obtained exhibited low apparent noise floors (on the order of tens of picoTesla) and retained a precision that enabled targeted modelling and removal of high-frequency noise with amplitudes of ±5 picoTesla. All of the different gradiometer configurations tested enabled recovery of most targets (including all major targets), although the horizontal configuration performed significantly worse in comparison.

Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

The detection of old submerged ammunition that is located on or below the seabed is important preparatory work for offshore projects. Multibeam echosounder, side scan sonar, subbottom profiler and magnetometers are the sensor types that are most commonly used for the task. Survey design decisions center around both detection campaign efficiency and sufficient data quality to find specific reference objects. The paper presents a comprehensive workflow for unexploded ordnance surveys in the sea and focuses on aspects of quantitative data quality control. For this purpose, data quality factors and corresponding threshold values were developed for each of the main sensor types. The authors designed and moderated a workshop-based stakeholder engagement process to establish expert consensus on suitable data quality factors and appropriate calculation of thresholds that define which data are sufficient and which are not. This approach was accompanied by a literature review of existing guidelines, standards, and survey recommendations. A quantitative description of data quality eases analyzing and comparing newly acquired and existing data. The paper presents the results of this process, thereby providing guidance for the planning, execution, and quality control of a technical investigation with the aim of finding unexploded ordnance in the sea. The defined data quality factors derived from the literature review and collective expert knowledge can be used in the suggested workflow.

Soil exploration using ground penetrating radar

Geophysical methods are extensively utilized in the field of geology and in geotechnical engineering such as seismic, gravitational, magnetic and electromagnetic fields. These methods are used to locate or to understand conditions below the ground surface, and the physical properties of subsurface. GPR also known as Radio Detecting and Ranging is based on the electromagnetic waves. It is a specially designed radar unit for transmitting electromagnetic pulses below the ground instead of air. In GPR the medium is soil which is heterogeneous and has higher attenuation rate than air. This method is used to measure the length, depth or to locate the soil layers and its deposits. GPR is one of the most versatile sensors; it provides high resolution profiles for shallow depth. GPR has been used in diverse fields such as archaeology, non-destructive testing, probing underground caves, detecting landmines, mapping pipes and conduits, investigating the reinforcement and conditions of roads, bridges and airport runways, to name a few. Use of this technique/method is being extensively adopted from recent years because of its properties and vast applications. The main applications of GPR in subsurface mapping are: mapping of subsurface utility structures, detection and mapping of unexploded ordnance and mines, extraction of hazardous waste containers or unexploded ammunitions, maintenance or repair of subsurface structures. This paper presents an understanding of the concept or the need of GPR dedicated to civil engineering applications in general and in the field of geotechnical engineering in particular.

IAS: A New Novel Phase-Based Filter for Detection of Unexploded Ordnances

A new phase-based filter (called improved analytical signal (IAS)) is introduced to detect buried unexploded ordnance (UXO) precisely from magnetic fields using the arcsine function of the ratio of the first-order vertical derivative of the analytical signal to the first-order derivatives of the x-, y-, and z-components of the analytical signal. The calculations are computed in the frequency domain and then transformed back into the space domain using the inverse Fourier transform. The filter has been tested on magnetic data collected at a test site with UXO bodies of variable orientation. It was also validated on magnetic data measured at a former army artillery range in Slovakia. The results show that the IAS filter not only revealed better imaging of the UXO bodies compared to the other commonly used filters but also produced a high-resolution image with much less influence of noise.