# Rethinking Evaluation of Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2509.16888 · HTML: https://mlsanity.com/paper/2509.16888/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [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)
- [UIU-Net: U-Net in U-Net for Infrared Small Object Detection](https://mlsanity.com/paper/2212.00968/) (2022)
- [Suppress and Balance: A Simple Gated Network for Salient Object Detection](https://mlsanity.com/paper/2007.08074/) (2020)
- [Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection](https://mlsanity.com/paper/1703.09157/) (2017)
- [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)
- [MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection](https://mlsanity.com/paper/2209.13756/) (2022)
- [ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection](https://mlsanity.com/paper/2310.20208/) (2023)
- [RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection](https://mlsanity.com/paper/2311.00917/) (2023)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention](https://mlsanity.com/paper/2408.03717/) (2024)
- [ABC: Attention with Bilinear Correlation for Infrared Small Target Detection](https://mlsanity.com/paper/2303.10321/) (2023)
- [Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better](https://mlsanity.com/paper/2506.12766/) (2025)
- [Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework](https://mlsanity.com/paper/2409.12448/) (2024)
- [Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline](https://mlsanity.com/paper/2312.02528/) (2023)
- [Power Battery Detection](https://mlsanity.com/paper/2508.07797/) (2025)

## Built on by (most cited first, 4 of 4)

- [An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation](https://mlsanity.com/paper/2605.21835/) (2026)
- [Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics](https://mlsanity.com/paper/2607.00886/) (2026)
- [RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection](https://mlsanity.com/paper/2606.01689/) (2026)
- [LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection](https://mlsanity.com/paper/2607.24184/) (2026)

## Abstract (arXiv)

As an essential vision task, infrared small target detection (IRSTD) has seen significant advancements through deep learning. However, critical limitations in current evaluation protocols impede further progress. First, existing methods rely on fragmented pixel- and target-level specific metrics, which fails to provide a comprehensive view of model capabilities. Second, an excessive emphasis on overall performance scores obscures crucial error analysis, which is vital for identifying failure modes and improving real-world system performance. Third, the field predominantly adopts dataset-specific training-testing paradigms, hindering the understanding of model robustness and generalization across diverse infrared scenarios. This paper addresses these issues by introducing a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation. These aim to offer a more thorough and rational hierarchical analysis framework, ultimately fostering the development of more effective and robust IRSTD models. An open-source toolkit has be released to facilitate standardized benchmarking.