Madrid, Spain
13 - 16 September 2021
Conference SD112
Artificial Intelligence and Machine Learning in Defense Applications III
Conference Committee
Important Dates
Abstract Due:
28 April 2021
Abstract due date has been extended. Submit your abstract now.

Author Notification:
18 June 2021

Manuscript Due Date:
16 August 2021

Call for
The main application of military imaging systems is situational awareness: knowing who and what is in the vicinity and gaining an understanding of their behavior. Image analysis techniques support the key tasks that enable situational awareness: detection, tracking, classification, identification and behavior recognition of targets or objects, while avoiding too many false alarms or missed detections. Artificial Intelligence and Machine Learning are increasingly used to assist in these tasks, as the amount of sensor data increases while there are fewer analysts and camera operators available.

This conference will focus on technology development in artificial intelligence and machine learning techniques for automatic and machine assisted image and video analysis for defense applications, including enhancement, target detection, classification/recognition, identification, tracking and threat assessment. Both model-based approaches and data-driven methods such as neural nets are considered. Sensors considered will include EO/IR, SAR, multi- and hyper-spectral imagers.

As in civil applications algorithms must be able to deal with noisy data and varying conditions. One of the additional challenges encountered, compared to civilian/commercial applications, relates to the fact that for defense applications only limited operational data is available for training, testing and evaluation. This is especially the case for event detection, where interesting events rarely occur. For defense applications, the technology will ideally be robust to inputs that are adversarial examples, i.e., inputs that are intentionally designed to cause the model to make a mistake. The processing should also be able to detect, classify and identify camouflaged objects. Evaluation and performance prediction of these algorithms for varying circumstances is also part of this conference.

Original papers are solicited in, but not limited to, the following topical areas:

Image Analysis Techniques
  • automatic target detection, classification, recognition and identification
  • automatic tracking
  • computational imaging
  • image enhancement (denoising, super-resolution , filtering etc)
  • inverse problems
  • sensor fusion
  • colorization.

  • Artificial Intelligence and Machine learning
  • machine learning and deep learning for image and video processing systems
  • transfer learning
  • alternate learning strategies such as semi-supervised learning and generative adversarial learning
  • hyper-parameter selection
  • the use of synthetic data for training
  • edge processing: low power (wattage) processing.

  • Robustness, Evaluation and Performance Prediction
  • robustness of algorithms to extended operating conditions
  • robustness of algorithms against adversarial examples
  • transparency and explainability of algorithms.

  • Defence Applications for these Types of Techniques
  • maritime situational awareness
  • unmanned sensor systems: UAVs, UGVs, UUVs
  • unattended sensors and systems
  • compound security and force protection
  • border protection
  • route clearance
  • reconnaissance and surveillance
  • vehicle situation awareness
  • route planning
  • improved visualization.
  • Conference Committee
    Conference Chair
    • Judith Dijk, TNO Defence, Security and Safety (Netherlands)

    Program Committee
    • Christopher R. Bell, Defence Science and Technology Lab. (United Kingdom)
    • Fabrizio Berizzi, European Defence Agency (Belgium)
    • David K.J. Gustafsson, FOI-Swedish Defence Research Agency (Sweden)
    • Michel Honlet, HENSOLDT Sensors GmbH (Germany)

    Program Committee continued...
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