Infrared Small

Infrared small target detection (IRSTD) focuses on identifying tiny objects in infrared imagery, a challenging task due to low signal-to-noise ratios and limited target features. Current research emphasizes improving detection accuracy and efficiency through novel deep learning architectures, including adaptations of generic segmentation models like SAM, YOLO-based approaches, and transformer-based networks incorporating attention mechanisms and multi-scale feature fusion. Advances in IRSTD have significant implications for various applications, such as autonomous driving, surveillance, and military reconnaissance, by enabling more robust and reliable object detection in challenging infrared imaging conditions.