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EDF-2022-LS-RA-CHALLENGE-DIGIT-HTDP: Unmanned ground and aerial systems for hidden threats detection – Participation to a technological challenge

Improvised explosive devices (IEDs) and landmines are a significant threat to military personnel, civilians and equipment, and a major cause of casualties for European forces during operations. Countering these hidden threats is essential to protect soldiers, reduce loss of equipment, secure critical logistic activities, improve mobility and freedom to act by increasing the security of operation areas, and more generally enhance operational efficiency. Furthermore, in a hybrid warfare context, these threats are increasingly used against civilian populations. In particular, they have the potential to severely disrupt both military and civilian supply chains, damage critical infrastructures and affect strategic lines of communication.

Detecting these hidden threats is a first essential step to counter them. Since they are by design difficult to detect for humans, automatic detection technologies can play an important role. However, the task is intrinsically difficult, and the performance of existing technologies is still far from answering the needs. Scenarios classically encountered by armed forces in past missions such as route clearance already represent a challenge. In addition, IEDs are increasingly used in urban scenarios where the detection is even more difficult, especially if multiple IEDs emplacements are used. There is a need to enhance detection technologies, especially for scenarios where single detection devices are not sufficient and the use of distributed sensors is deemed useful. There is also a need to determine their type (e.g., how they are triggered), in particular to ease their neutralisation (rendering safe, disabling or destroying).

While the above issues have been the subject of much research over many years, progress is hindered by the lack of standardised benchmarks, and there is a need to evaluate the performances of integrated functional demonstrators in an objective and comparable manner, using representative testing environments and well-defined metrics.

Overall progress in IED and landmine detection and characterisation can be driven by progress along several lines:

  • Physics-based sensors enhancement;
  • Collection of representative data, combined with various artificial intelligence (AI) techniques, e.g., computer vision for object detection and localisation;
  • Use of various sensors borne by a fleet of unmanned ground and aerial systems, combined with information fusion techniques;
  • Better exploitation of limited amounts of data and use of models that are easier to adapt to new environments (through innovative AI techniques such as learning methods requiring less supervision from expert developers, transfer learning…);
  • Multidisciplinary cooperation between the hardware sensors and AI communities.

Proposals should address technological solutions to detect and characterise IEDs and landmines in complex environments, using a combination of advanced sensors, information fusion from these sensors, and unmanned ground and aerial systems to extend the detection capabilities. These solutions should be evaluable through the testing environment set up in the framework of the technological challenge.

Proposals should include clear descriptions of criteria to assess work package completion. Criteria should include the participation to the test campaigns organised in the framework of the technological challenge, the delivery of sensor data collected during the field tests, and the delivery of descriptions of the systems submitted to the tests.

The proposals must address in particular the following as part of the mandatory activities:

  • Research on new approaches and technologies for hidden threat detection and characterisation
  • Participation to the evaluation campaigns organised in the framework of the technological challenge, including:
    • Contribution to the exchanges with other stakeholders on the evaluation plans
    • Submission of the systems to experimental performance measurements during the field and online test campaigns managed by the challenge organisers
    • Collection and sharing of data
    • Participation to debriefing workshops

Functional requirements

The proposed solutions should fulfil the following requirements:

  • Ability to go through a zone with IEDs or landmines while minimizing the risk of damage
  • Ability to detect and map IEDs and landmines in a given area, with maximum accuracy
  • Ability to characterise IEDs and landmines, with maximum accuracy

The performances for these abilities should be measurable through the test campaign conducted in the framework of the technological challenge, using protocols and metrics based on those described in the preliminary evaluation plan provided as part of the call documents. Details about how the proposed approaches and systems will address the tasks outlined in the preliminary evaluation plan should be described in the proposals.

Systems should be able to record the data acquired through their sensors, in order to enable reproduction of experiments in a software environment. The types of data that could be shared with other teams should be described in the proposals.

While much flexibility is left concerning the system configuration for the challenge, systems should be designed to experiment operationally relevant solutions.

Expected impact

The expected impacts are:

  • Enhanced clarity on performances of equipment for IED and landmine detection and characterisation
  • Availability of databases to further develop and test equipment
  • Enhanced soldier protection and increased survivability, through reduced risk for lethal or damaging incidents
  • Enhanced freedom of action
  • Reduced risks of disruption of strategic infrastructures
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EDF-2022-LS-RA-CHALLENGE-DIGIT-HTDO: Unmanned ground and aerial systems for hidden threats detection – Organisation of a technological challenge

IED and landmine detection has been a research topic for many years. However, progress is hindered by the lack of standardised benchmarks. There is a need to rely on representative testing environments enabling an objective and comparable evaluation of developed systems.

Furthermore, field tests cannot be repeated at will and are not perfectly reproducible, especially for detection systems that involve artificial intelligence. Online tests of software components, for which measurements are easily reproducible and which enable short development cycles, should therefore also be organised. Since little data is readily available, data for online tests need to be collected during field tests organised previously during the challenge. This combination of field tests and online tests is needed to steer fast progress toward operational goals.

Proposals should address the organisation of a technological challenge on IED and landmine detection based on the preliminary evaluation plan provided as part of the call documents. This includes the collection of data recorded by the participating teams during field tests, the annotation of this data and the sharing of the resulting databases.

Proposals should include clear descriptions of criteria to assess work package completion. Criteria should include the production of detailed evaluation plans agreed upon by all stakeholders, the production of the annotated databases needed for the evaluations, the production of measurements for all systems submitted to the tests by the participating teams following these plans, and the organisation of the needed events.

The proposals must address in particular the following as part of the mandatory activities:

  • Setting up of the hardware and software infrastructures for testing hidden threat detection and characterisation technologies in the framework of the technological challenge
  • Collection of sensor data from the participating teams, annotation of the data with ground truth information, and quality assessment, distribution and curation of databases
  • Organisation of the evaluation campaigns, and in particular
    • Coordination of the exchanges with other stakeholders on the evaluation plans and elaboration of these plans
    • Management of the experimental hardware and software test campaigns and of the objective measurements of the performances of the systems submitted to the tests by the participating teams according to the protocols and metrics described in the evaluation plans
    • Organisation of the debriefing workshops

Functional requirements

The proposed solutions should enable to measure the performances of the tested systems according to detailed evaluation plans based on the preliminary evaluation plan provided as part of the call documents. Key aspects of the foreseen detailed evaluation plans and associated data management should be described in the proposals. Proposals should in particular describe:

  • scenarios, nature and size of test ranges, and environmental conditions,
  • types of devices, concealment, attack geography,
  • nature and volume of data annotation,
  • the framework for trusted sharing of data,
  • the detailed planning of the test campaigns, including how runs can be organised in parallel on several test ranges,
  • evaluation procedures (rules and tools to implement the metrics) and significance tests performed on measurements.

The testing environment should be able to accommodate for up to six participating teams.

During the challenge, drafts of the detailed evaluation plans should be submitted for discussion to the participating teams and to any stakeholder designated by the funding authority, early enough to take into account the feedback for the actual evaluation campaigns. Any evolution of the evaluation plans should take into account several factors: technical possibilities and cost, scientific relevance of the measurement, and representativeness of the metrics and protocols with respect to military needs. The justification of any change that is not subject to a consensus should be documented.

Expected impact

The expected impacts are

  • Enhanced metrics and protocols to measure progress of R&D on IED and landmine detection and characterisation
  • Standardisation of combined online and field testing for IED and landmine detection and characterisation
  • Availability of databases to further develop and test equipment
  • Enhanced clarity of system performances for all stakeholders, including system developers, funders and users
  • Enhanced community building for the
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EDF-2022-RA-DIGIT-DBIR: Shared databases and integrated systems for image recognition

Image recognition technologies become essential for defence applications. There is in particular an increasing need for forces to analyse their environment more efficiently in order to enhance decision-making, responsiveness and survivability while ensuring the observation function effectively. This need is reinforced by the emergence of new forms of threats such as hypersonic, swarming, miniaturised or stealth weapons, which require increased speed, sensitivity or accuracy of the recognition systems. This need applies to manned and unmanned platforms as well as to wide-area or long-lasting surveillance. Besides, databases are essential for training, testing and certifying artificial intelligence (AI) systems such as image recognition systems. However, collecting data that is both representative of military operational scenarios and sharable for AI system development is a complex task.

Furthermore, data annotation (e.g., definition of regions in images, labelling…) and curation need significant efforts that are often underestimated. The lack of specialised entities missioned to serve the community by actively creating representative and sharable databases further hinders the creation of such databases. These issues are often a bottleneck in system development. Frameworks should be developed that enable or facilitate cooperation and sharing of image databases for defence.

In addition, new high-resolution sensor technologies provide larger amounts of information that are difficult to transmit in their entirety in real time. Automatic processing located near the sensor is needed to reduce the information flow. This requires joint optimisation of software and hardware and can involve trade-offs between recognition performances and integration constraints.

Proposals should address the development and objective testing of image recognition systems for defence, the creation of the needed new databases, and the integration of the developed image recognition technologies near the sensors and objective testing of these integrated systems. Any relevant types of images and sensors (visible, infrared, multiband, hyperspectral…) and any well-defined types of recognition tasks (detection and classification for well-defined classes of objects, detection and identification of known objects, tracking…) can be considered.

The proposals must address in particular the following:

  • Creation and sharing of annotated image databases, and development of appropriate frameworks for that purpose
  • Development of software image recognition systems
  • Integration of such software systems on customised hardware near sensors (integrated technology demonstrators)
  • Objective evaluation of the performances of the software and integrated systems

Functional requirements

The proposed solutions should fulfil the following requirements.

  • A limited number of well-defined use cases should be addressed (possibly one).
  • For each use case addressed:
    • The use case should address well-identified military operational needs and
    • The use case should be defined by clear evaluation data, metrics and protocols described in the proposal. Evaluation raw data should be real images directly acquired through However, if real images do not yet exist (for instance for future threats) but hybrid or synthetic images can be expected to be representative of the anticipated threats, such images may be used.
    • Several approaches should be explored by different research teams (while being evaluated in a comparable way using the above-mentioned data, metrics and protocols). The proposed techniques should be presented in the proposal.
    • The state-of-the-art should be described in the proposal, relying as much as possible on past objective and quantitative evaluation results. The expected progress beyond the state-of-the-art should also be described, taking into account the foreseen amount of new training data and/or the ability to make a better use of existing data through innovative learning techniques, and a
    • possible roadmap toward technological maturity beyond the project should be
    • The needed image data should be collected, relying on dedicated trials and measurement campaigns as needed. Images should be annotated using documented annotation guidelines. The resulting databases should be shared at least with the project partners who need them in the framework of the project.
    • The possibility to share and reuse these databases beyond the project should be anticipated, including where they would be classified. The organisational and technical framework for data production and sharing should be described in the proposal. In particular, the entity in charge of curating and distributing the databases should be clearly identified and the conditions for sharing should be described. If hybrid or synthetic images are needed for system evaluation, the possibility to share the tools used to generate these images should be anticipated in the same conditions.
    • Setting up a framework for data production and sharing that can be reused beyond the project is encouraged. Synergies with similar efforts at the European level should be sought.
    • Training and evaluation data should be representative of the use case and cover the various conditions encountered in real-life scenarios (e.g., various climate, weather or lighting conditions, various types of background landscapes…).
    • If representative data that can be collected by users during operations is deemed needed to reach the expected system performance, machine-learning techniques to learn continuously from user supervision (user-driven adaptation) should be considered.
    • Software recognition systems should be optimised to offer the best possible recognition performance (e.g., high probability of correct detection and low false alarm rates, high area under the ROC9 curve…).
    • Integrated recognition systems should maintain the recognition performance of software systems as much as possible while taking into account size, weight, power and cost constraints.
    • Both software and integrated systems should be benchmarked using the agreed-upon evaluation data, metrics and protocols.

Expected impact

The expected impacts are:

  • Shareable databases for image recognition
  • Established frameworks easing the production and sharing of databases, creation or reinforcement of entities producing sharable databases
  • Availability of new integrated image processing products
  • Enhanced decision-making and responsiveness, reduction of cognitive load of soldiers during operations
  • Enhanced situational awareness
  • Enhanced safety, resilience and survivability
  • Reduction of fratricides and collateral damages
  • Enhanced unmanned system autonomy

9 Receiver Operating Characteristic

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