23 citations · 27 across the 5 of their papers we have counts for
5 papers
Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection
Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre
Infrared Small Target Detection (IRSTD) is a challenging task in defense applications, where complex backgrounds and tiny target sizes often result in numerous false alarms using c…
Self-Supervised Learning for Real-World Object Detection: a Survey
Alina Ciocarlan, Sidonie Lefebvre, Sylvie Le Hégarat-Mascle +1
Self-Supervised Learning (SSL) has emerged as a promising approach in computer vision, enabling networks to learn meaningful representations from large unlabeled datasets. SSL meth…
Robust infrared small target detection using self-supervised and a contrario paradigms
Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre +1
Detecting small targets in infrared images poses significant challenges in defense applications due to the presence of complex backgrounds and the small size of the targets. Tradit…
Paradigm for YOLO-based Infrared Small Target Detection
Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre +2
Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgr…
Deep-NFA: a Deep Framework for Small Object Detection
Alina Ciocarlan, Sylvie Le Hegarat-Mascle, Sidonie Lefebvre +1
The detection of small objects is a challenging task in computer vision. Conventional object detection methods have difficulty in finding the balance between high detection and low…