Deep-NFA: A deep a contrario framework for tiny object detection

Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle

Abstract: Highlights•Detecting tiny objects is challenging; they are often hidden in background clutter.•We design an a contrario-based module to describe tiny object unexpectedness.•Our add-on module can guide the training loop of any segmentation neural network.•It leads to competitive results on infrared small target and road crack detection.•It allows for an intuitive control of false alarms and the results are interpretable.