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.

利用 “对原则” 决策标准将统计标准添加到 YOLOv7-tiny 中，从而使其在红外小目标检测和目标检测网络之间填补了性能差距，并显著增强了 YOLO 在少样本情况下的鲁棒性。

基于 YOLO 的红外小目标检测中的 $a ~ contrario$ 范式

$\textit{A Contrario}$ Paradigm for YOLO-based Infrared Small Target  Detection

Recently, segmentation neural networks have been significantly improved by
demonstrating very promising accuracies on public benchmarks. However, these
models are very heavy and generally suffer from low inference speed, which
limits their application scenarios in practice. Meanwhile, existing fast
segmentation models usually fail to obtain satisfactory segmentation accuracies
on public benchmarks. In this paper, we propose a teacher-student learning
framework that transfers the knowledge gained by a heavy and better performed
segmentation network (i.e. teacher) to guide the learning of fast segmentation
networks (i.e. student). Specifically, both zero-order and first-order
knowledge depicted in the fine annotated images and unlabeled auxiliary data
are transferred to regularize our student learning. The proposed method can
improve existing fast segmentation models without incurring extra computational
overhead, so it can still process images with the same fast speed. Extensive
experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate
that the proposed teacher-student learning framework is able to significantly
boost the performance of student network.

本文提出一种基于零、一阶知识的细分神经网络迁移学习教师 - 学生框架，将拥有更好表现的重型细分网络的知识转移到轻型细分网络上，提高了轻型网络的细分准确性，仍然保持快速特性，并经数据实验验证该框架可以显著提升学生网络性能。