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

Autor:	Ciocarlan, Alina, Hégarat-Mascle, Sylvie Le, Lefebvre, Sidonie, Woiselle, Arnaud, Barbanson, Clara
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.02288 Zobrazit plný text záznamu View this record from Arxiv