7 papers · 1 filter
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…
Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap
Elfi I. S. Hofmeijer, Ella P. Fokkinga, Friso G. Heslinga +2
Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type, operational environment, or…
RGB-to-IR image translation for infrared vehicle detection in unseen UAV domains
Thijs A. Eker, Ella P. Fokkinga, Jan Erik van Woerden +4
Synthetic training data is crucial for developing vision AI when real-world data is scarce, as in thermal infrared (IR) aerial vehicle detection. While abundant UAV RGB imagery mot…
Domain shift-robust object detection with GenAI image editing
Isabel D. Stein, Thijs A. Eker, Sebastiaan P. Snel +4
Object detectors often degrade under domain shifts such as changes in lighting, weather, or occlusion. These shifts alter object appearance and expose a reliance on visual shortcut…
Class-specific diffusion models improve military object detection in a low-data domain
Ella P. Fokkinga, Jan Erik van Woerden, Thijs A. Eker +4
Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this work, we investigate whether…
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…
Aligning Object Detector Bounding Boxes with Human Preference
Ombretta Strafforello, Osman S. Kayhan, Oana Inel +2
Previous work shows that humans tend to prefer large bounding boxes over small bounding boxes with the same IoU. However, we show here that commonly used object detectors predict l…