# Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2510.04741 · HTML: https://mlsanity.com/paper/2510.04741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Asymmetric Contextual Modulation for Infrared Small Target Detection](https://mlsanity.com/paper/2009.14530/) (2020)
- [Dense Nested Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2106.00487/) (2021)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Infrared Small Target Detection with Scale and Location Sensitivity](https://mlsanity.com/paper/2403.19366/) (2024)
- [SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection](https://mlsanity.com/paper/2401.15583/) (2024)
- [RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection](https://mlsanity.com/paper/2208.08738/) (2022)
- [Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection](https://mlsanity.com/paper/2412.16986/) (2024)
- [EFLNet: Enhancing Feature Learning for Infrared Small Target Detection](https://mlsanity.com/paper/2307.14723/) (2023)
- [DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection](https://mlsanity.com/paper/2409.19599/) (2024)
- [Efficient Evaluation of the Number of False Alarm Criterion](https://mlsanity.com/paper/1807.03594/) (2018)
- [Variational Autoencoder for Anomaly Detection: A Comparative Study](https://mlsanity.com/paper/2408.13561/) (2024)

## Abstract (arXiv)

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.