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The traditional mine remote sensing information pre-survey is mainly based on manual interpretation, and interpreters delineate the mine boundary shape. This work is difficult and susceptible to subjective judgment due to the large differences in the characteristics of mining complex within individuals and small differences between individuals. CondInst-VoV and BlendMask-VoV, based on VoVNet-v2, are two improved instance segmentation models proposed to improve the efficiency of mine remote sensing pre-survey and minimize labor expenses. In Hubei Province, China, Gaofen satellite fusion images, true-color satellite images, false-color satellite images, and Tianditu images are gathered to create a Key Open-pit Mine Acquisition Areas (KOMMA) dataset to assess the efficacy of mine detection models. In addition, regional detection was carried out in Daye Town. The result shows that the performance of improved models on the KOMMA dataset exceeds the baseline as well as the verification accuracy of manual interpretation in regional mine detection tasks. In addition, CondInst-VoV has the best performance on Tianditu image, reaching 88.816% in positioning recall and 98.038% in segmentation accuracy.

Unmanned mine detection vehicles (UMDVs) have been used for military missions to detect and deactivate mines and reduce military and civilian casualties. UMDVs must cover an area of several kilometers to detect mines. Hence, a central coordination system is required for these UMDVs, and it has been achieved via the development of a vehicular ad hoc network (VANET). UMDVs can communicate with each other by broadcasting mine detection messages (MDMs) to incoming vehicles in VANET without infrastructure installations. Therefore, a novel mine detection vehicle routing (MDVR) protocol has been developed to create an ad hoc communication network among UMDVs. The protocol performs cluster-based multicast communication in real time. It adapts to dynamic scenarios by proposing a priority-based cluster head election scheme (PBCHE) and introducing cluster adaptability level schemes. Network simulator results show that the MDVR protocol can reduce the overhead and delay in MDM dissemination. In addition, the MDVR protocol shows promising results in terms of throughput, packet delivery ratio, and cluster stability.

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naïve Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.

The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that differences of the target response between sub-bands can be captured. Then, exploiting the low-rank and sparse property of the image tensor, we use the recently proposed Tensor Robust Principal Analysis to remove clutter by decomposing the image tensor into three components: a low-rank component containing clutter, a sparse component capturing target response, and noise. Finally, target detection is accomplished by applying thresholds to the extracted target image. Numerical simulations and experiments with different GPR systems are conducted. The results show that the proposed method effectively improves signal-to-clutter ratio by more than 20 dB and yields satisfactory results with high probability of detection and low false alarm rates.

Pouched rats were employed as mine‐detection animals in a quality‐control application where they searched for mines in areas previously processed by a mechanical tiller. The rats located 58 mines and fragments in this 28,050‐m2 area with a false indication rate of 0.4 responses per 100 m2. Humans with metal detectors found no mines that were not located by the rats. These findings indicate that pouched rats can accurately detect land mines in disturbed soil and suggest that they can play multiple roles in humanitarian demining.

Metal detection technology based on the principle of electromagnetic induction has always played an important role in the field of mine detection. In metal detectors, the probe coil is an essential component, and its performance affects the overall performance of the system. This paper introduces the basic principles of pulsed electromagnetic induction and constructs a model of the metal detector probe using the finite element electromagnetic simulation software Ansys Maxwell. The study explores the impact of the coil shape and radius, the number of turns, and the coil current on the primary field generated by the probe coil, as well as the influence of different metallic target materials on the secondary field received by the coil. Finally, the effectiveness of the simulation is verified by the experiments. The research findings have certain reference significance for optimizing the best parameter configuration of the probe coil.

Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a suspicious area. It is generally divided into four stages: detection, classification, identification and disposal. The detection and classification steps are usually performed using a sonar mounted on a ship’s hull or on an underwater vehicle. After retrieving the sonar data, military personnel scan the seabed images to detect targets and classify them as mine-like objects (MLOs) or benign objects. To reduce the technical operator’s workload and decrease post-mission analysis time, computer-aided detection (CAD), computer-aided classification (CAC) and automated target recognition (ATR) algorithms have been introduced. This paper reviews mine detection and classification techniques used in the aforementioned systems. The author considered current and previous generation methods starting with classical image processing, and then machine learning followed by deep learning. This review can facilitate future research to introduce improved mine detection and classification algorithms.

In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

This work presents an enhanced autonomous airborne Synthetic Aperture Radar (SAR) imaging system able to provide full 3D radar images from the subsurface. The proposed prototype and methodology allow the safe detection of both metallic and non-metallic buried targets even in difficult-to-access scenarios without interacting with the ground. Thus, they are particularly suitable for detecting dangerous targets, such as landmines and Improvised Explosive Devices (IEDs). The prototype is mainly composed by an Ultra-Wide-Band (UWB) radar module working from Ultra-High-Frequency (UHF) band and a high accuracy dual-band Real Time Kinematic (RTK) positioning system mounted on board an Unmanned Aerial Vehicle (UAV). The UAV autonomously flies over the region of interest, gathering radar measurements. These measurements are accurately geo-referred so as to enable their coherent combination to obtain a well-focused SAR image. Improvements in the processing chain are also presented in order to deal with some issues associated to UAV-based measurements (such as non-uniform acquisition grids) as well as to enhance the resolution and the signal to clutter ratio of the image. Both the prototype and the methodology were validated with measurements, showing their capability to provide high-resolution 3D SAR images.

Commonly found landmines, such as the TM-62M, MON-100, and PDM-1, in the recent Russia–Ukraine war confirm the continued use of metals in munitions. Traditional demining techniques, primarily relying on handheld metal detectors and Ground Penetrating Radar (GPR) systems, remain state of the art for subsurface detection. However, manual demining with handheld metal detectors can be slow and pose significant risks to operators. Drone-based metal detection techniques offer promising solutions for rapid and effective landmine detection, but their reliability and accuracy remain a concern, as even a single missed detection can be life-threatening. This study evaluates the potential of an airborne metal detection system as an alternative to traditional handheld detectors. A comparative analysis of three distinct metal detectors for landmine detection is presented: the EM61Lite, a sensitive airborne metal detection system (tested in a pseudo-drone-based scenario); the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. The comparison focuses on the number of metallic targets each detector identifies in a controlled test field containing inert landmines and UXOs. Our findings highlight the strengths and limitations of airborne metal detection systems like the EM61Lite and emphasize the need for advanced processing techniques to facilitate their practical deployment. We demonstrate how our experimental normalization technique effectively identifies additional anomalies in airborne metal detector data, providing insights for improved detection methodologies.