6 papers
Beyond Benchmarks: Using VLMs to Reveal Systematic Classification Failures Under Real World Conditions
Dieuwertje Alblas, Alma M. Liezenga, Jan Erik van Woerden +3
Verification and validation (V&V) of classification models is crucial to enable a wide range of sensor processing applications. Currently, the V&V process relies on time-consuming…
Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann +12
Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial in…
Training with synthetic data for drone detection in thermal imagery
Tanel Liiv, Sander Soodla, Nzamba Bignoumba +2
Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, an…
AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain
Alma M. Liezenga, Stefan Wijnja, Puck de Haan +4
Poisoning attacks pose an increasing threat to the security and robustness of Artificial Intelligence systems in the military domain. The widespread use of open-source datasets and…
Occlusion Robustness of CLIP for Military Vehicle Classification
Jan Erik van Woerden, Gertjan Burghouts, Lotte Nijskens +4
Vision-language models (VLMs) like CLIP enable zero-shot classification by aligning images and text in a shared embedding space, offering advantages for defense applications with s…
Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology
Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga +6
Collecting and annotating real-world data for the development of object detection models is a time-consuming and expensive process. In the military domain in particular, data colle…