  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 backgrounds. Traditional object detection methods such as
YOLO struggle to detect tiny objects compared to segmentation neural networks,
resulting in weaker performance when detecting small targets. To reduce the
number of false alarms while maintaining a high detection rate, we introduce an
$\textit{a contrario}$ decision criterion into the training of a YOLO detector.
The latter takes advantage of the $\textit{unexpectedness}$ of small targets to
discriminate them from complex backgrounds. Adding this statistical criterion
to a YOLOv7-tiny bridges the performance gap between state-of-the-art
segmentation methods for infrared small target detection and object detection
networks. It also significantly increases the robustness of YOLO towards
few-shot settings.
