Guidebook on Detection Technologies and Systems for Humanitarian Demining

The aim of this publication is to provide the mine action community, and those supporting mine action, with a consolidated review and status summary of detection technologies that could be applied to humanitarian demining operations. This Guidebook is meant to provide information to a wide variety of readers. For those not familiar with the spectrum of technologies being considered for the detection of landmines and for area reduction, there is a brief overview of the principle of operation for each technology as well as a summary listing of the strengths, limitations, and potential for use of the technology to humanitarian demining. For those with an intermediate level of understanding for detection technologies, there is information regarding some of the more technical details of the system to give an expanded overview of the principles involved and hardware development that has taken place. Where possible, technical specifications for the systems are provided. For those requiring more information for a particular system, relevant publications lists and contact information are also provided

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

Test and Evaluation of Japanese GPR-based AP Mine Detection Systems Mounted on Robotic Vehicles

This article introduces Japanese activities regarding a project, “Research and Development of Sensing Technology, Access and Control Technology to Support Humanitarian Demining of AP Mines.” This project, which includes the research of six teams from academia and industry, has been funded by the Japan Science and Technology Agency (JST) under the auspices of the Ministry of Education, Culture, Sports, Science and Technology (MEX T). The developed systems are equipped with both groundpenetrating radar and a metal detector, and they are designed to make no explicit alarm and to leave decision-making of detection using subsurface images to the operators. To evaluate these kinds of systems, a series of trials was conducted in Japan from 8 February to 11 March 2005

Data Fusion for Close‐Range Detection

Two approaches for combining humanitarian mine detection sensors are described in parallel, one based on belief functions and the other one based on possibility theory. In a first step, different measures are extracted from the sensor data. After that, based on prior information, mass functions and possibility distributions are derived. The combination of possibility degrees, as well as of masses, is performed in two steps. The first one applies to all measures derived from one sensor. The second one combines results obtained in the first step for all sensors used. Combination operators are chosen to account for different characteristics of the sensors. Comparison of the combination equations of the two approaches is performed as well. Furthermore, selection of the decision rules is discussed for both approaches. These approaches are illustrated on a set of real mines and non‐dangerous objects and using three sensors: an infrared camera, an imaging metal detector and a ground‐penetrating radar

A Survey of Research on Sensor Technology for Landmine Detection

According to official figures, more than 100 million landmines lie buried around the world. Although intended for warfare, these mines remain active after warfare ends. Each day these mines are triggered accidentally by civilian activities, ravaging the land and killing or maiming innocent people. To help stop this destruction of the environment and humanity, the scientific community must develop effective humanitarian demining. Mine detection is especially vital to humanitarian demining. The goal of military demining is to clear enough mines quickly to allow troops through a land area. Military demining usually requires mine destruction rates of 80%. The goal of humanitarian demining, in contrast, is to clear enough mines to permit normal civilian use of the land (e.g., construction or agriculture). Humanitarian demining thus demands a destruction rate approaching perfection: UN specifications require a rate better than 99.6%. Of course, a critical aspect of mine clearance is mine detection. Before one can remove mines, one must locate them. To aid scientific inquiry into mine detection, this paper reviews the major current and developing technologies for mine detection. We do not claim to include every technology. Often the details of research intended for specific military applications are difficult to attain. This paper highlights significant studies of mine detection technologies, discussed in several recent conferences and in many recent articles and reports, to show promising directions for future research