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

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. …

computer computer vision cs.cv detection detection methods images networks neural networks objects paradigm performance segmentation small struggle targets vision yolo