  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 false alarm rates. In the literature, some
methods have addressed this issue by enhancing the feature map responses, but
without guaranteeing robustness with respect to the number of false alarms
induced by background elements. To tackle this problem, we introduce an
$\textit{a contrario}$ decision criterion into the learning process to take
into account the unexpectedness of small objects. This statistic criterion
enhances the feature map responses while controlling the number of false alarms
(NFA) and can be integrated into any semantic segmentation neural network. Our
add-on NFA module not only allows us to obtain competitive results for small
target and crack detection tasks respectively, but also leads to more robust
and interpretable results.
