A Contrario\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. 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.Comment: Accepted to ICASSP 202

Quantum Noise in Multipixel Image Processing

We consider the general problem of the quantum noise in a multipixel measurement of an optical image. We first give a precise criterium in order to characterize intrinsic single mode and multimode light. Then, using a transverse mode decomposition, for each type of possible linear combination of the pixels' outputs we give the exact expression of the detection mode, i.e. the mode carrying the noise. We give also the only way to reduce the noise in one or several simultaneous measurements.Comment: 8 pages and 1 figur