# Refined Infrared Small Target Detection Scheme with Single-Point Supervision

arXiv: https://arxiv.org/abs/2408.02773 · HTML: https://mlsanity.com/paper/2408.02773/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2304.01484/) (2023)
- [Multi-Scale Direction-Aware Network for Infrared Small Target Detection](https://mlsanity.com/paper/2406.02037/) (2024)
- [Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework](https://mlsanity.com/paper/2407.20090/) (2024)

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

- [From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2412.11154/) (2024)
- [The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results](https://mlsanity.com/paper/2408.09615/) (2024)
- [Rethinking Infrared Small Target Detection: A Foundation-Driven Efficient Paradigm](https://mlsanity.com/paper/2512.05511/) (2025)
- [Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels](https://mlsanity.com/paper/2610.05918/) (2026)

## Abstract (arXiv)

Recently, infrared small target detection with single-point supervision has attracted extensive attention. However, the detection accuracy of existing methods has difficulty meeting actual needs. Therefore, we propose an innovative refined infrared small target detection scheme with single-point supervision, which has excellent segmentation accuracy and detection rate. Specifically, we introduce label evolution with single point supervision (LESPS) framework and explore the performance of various excellent infrared small target detection networks based on this framework. Meanwhile, to improve the comprehensive performance, we construct a complete post-processing strategy. On the one hand, to improve the segmentation accuracy, we use a combination of test-time augmentation (TTA) and conditional random field (CRF) for post-processing. On the other hand, to improve the detection rate, we introduce an adjustable sensitivity (AS) strategy for post-processing, which fully considers the advantages of multiple detection results and reasonably adds some areas with low confidence to the fine segmentation image in the form of centroid points. In addition, to further improve the performance and explore the characteristics of this task, on the one hand, we construct and find that a multi-stage loss is helpful for fine-grained detection. On the other hand, we find that a reasonable sliding window cropping strategy for test samples has better performance for actual multi-size samples. Extensive experimental results show that the proposed scheme achieves state-of-the-art (SOTA) performance. Notably, the proposed scheme won the third place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 1: Weakly Supervised Infrared Small Target Detection".