An anomaly-aware detection head for frugal and robust Infrared Small Target Detection

Infrared Small Target Detection (IRSTD) is a challenging task in defense applications, where complex backgrounds and tiny target sizes often result in numerous false alarms using conventional object detectors. To overcome this limitation, we propose an Anomaly-Aware version of You Only Look Once (YOLO) detector (namedAA-YOLO), which integrates a statistical anomaly detection test into its detection head. By treating small targets as unexpected patterns against the background, AA-YOLO effectively controls the false alarm rate. Our approach not only achieves competitive performance on several IRSTD benchmarks, but also demonstrates remarkable robustness in scenarios with limited training data, noise, and domain shifts. Furthermore, since only the detection head is modified, our design is highly generic and has been successfully applied across various YOLO backbones, including lightweight models. It also provides promising results when integrated into an instance segmentation YOLO. This versatility makes AA-YOLO an attractive solution for real-world deployments where resources are constrained. The code is available at https://github.com/AMIAD-Research/AA-YOLO . • We present AA-YOLO, a YOLO model with anomaly testing for IR small target detection. • AA-YOLO attains state-of-the-art detection of small targets in complex IR scenes. • Our method is robust across varied scenarios: limited data, noise, and domain shifts. • AA-YOLO is lightweight and works with many YOLO backbones, ideal for real-world use.