Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection
This addresses the challenge of reliable small target detection in defense scenarios, offering a frugal and robust solution, though it appears incremental as it modifies an existing YOLO framework.
The paper tackled the problem of high false alarm rates in infrared small target detection for defense applications by proposing Anomaly-Aware YOLO, which integrates statistical anomaly detection into the detection head, achieving competitive performance on benchmarks and robustness with limited data, noise, and domain shifts.
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 Anomaly-Aware YOLO (AA-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 will be publicly released.