Mine Detection Technology Research Articles

Research on mine vehicle tracking and detection technology based on YOLOv5

Vehicle tracking detection, recognition and counting is an important part of vehicle analysis. Designing such a model with excellent performance is difficult. The traditional target detection algorithm based on artificial features has poor generalization ability and robustness. In order to take use the deep learning method for vehicle tracking detection, recognition and counting, this paper proposes a vehicle detection method based on yolov5. This method uses the deep learning technology, takes the running vehicles video as the research object, analysis the target detection algorithm, proposes a vehicle detection framework and platform. The relevant detection algorithm of the platform designed in this paper has great adaptability, when displayed under various conditions, such as heavy traffic, night environment, multiple vehicles overlap with each other, partial loss of vehicles, etc. it has good performance. The experimental results show that the algorithm can accurately segment and identify vehicles according to the edge contour of vehicles. It can take use the materials includes pictures, videos, and real-time monitoring, and has a high recognition rate in real-time performance.

Applied behavior analysis is ideal for the development of a land mine detection technology using animals.

The detection and subsequent removal of land mines and unexploded ordnance (UXO) from many developing countries are slow, expensive, and dangerous tasks, but have the potential to improve the well-being of millions of people. Consequently, those involved with humanitarian mine and UXO clearance are actively searching for new and more efficient detection technologies. Remote explosive scent tracing (REST) using trained dogs has the potential to be one such technology. However, details regarding how best to train, test, and deploy dogs in this role have never been made publicly available. This article describes how the key characteristics of applied behavior analysis, as described by Baer, Wolf and Risley (1968, 1987), served as important objectives for the research and development of the behavioral technology component of REST while the author worked in humanitarian demining.

A survey of land mine detection technology

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50–100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state‐of‐the‐art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.

Mine action: status of sensor technology for close‐in and remote detection of anti‐personnel mines

ABSTRACTAfter a broad introductory discussion on the mine action problem, this paper presents the status of close‐in mine detection technologies, including operational characteristics, without aiming at being exhaustive. Signal processing aspects and important lessons on data fusion are also discussed briefly. The detection is considered as a global process in which the outputs of the sensors, considered as skilled specialists, are integrated in a fusion operation. Next, the paper briefly addresses the problem of area reduction using remote sensing. In this case, information, collected with appropriate sensors and associated with context information from the field, is integrated in a geographical information system. This part of the paper is intentionally limited to a very short description of the SMART project funded by the European Commission, which uses multispectral and full polarimetric radar data in order to assist image analysts in their interpretation of mined scenes during an area reduction process.

Physics of acoustic-to-seismic coupling and detection of buried objects

Airborne acoustic waves coupled into the surface of the ground excite Biot type I and II compressional and shear waves. If a mine-like target or other inhomogeneity is present below the surface, the ground surface vibrational velocity will show distinct changes due to reflection and scattering of these waves. Sound waves with a wavelength comparable to the object size are suitable for recognizing geometrical shapes of targets, while true wave-like acoustic scattering phenomena can be observed with a shorter acoustic wavelength. In this paper, a review of porous material physics relevant to mine detection will be presented. Recent development in the acoustic technology for mine detection will be reported. Taking advantage of a noncontact measurement technique, the surface vibrational velocity is detected with a laser Doppler vibrometer. [This work is supported by U.S. Army Communications-Electronics Command.]