# ISTD-YOLO: A Multi-Scale Lightweight High-Performance Infrared Small Target Detection Algorithm

arXiv: https://arxiv.org/abs/2504.14289 · HTML: https://mlsanity.com/paper/2504.14289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [EFLNet: Enhancing Feature Learning for Infrared Small Target Detection](https://mlsanity.com/paper/2307.14723/) (2023)
- [CenterNet++ for Object Detection](https://mlsanity.com/paper/2204.08394/) (2022)
- [Slim-neck by GSConv: A lightweight-design for real-time detector architectures](https://mlsanity.com/paper/2206.02424/) (2022)
- [textitA Contrario Paradigm for YOLO-based Infrared Small Target Detection](https://mlsanity.com/paper/2402.02288/) (2024)

## Abstract (arXiv)

Aiming at the detection difficulties of infrared images such as complex background, low signal-to-noise ratio, small target size and weak brightness, a lightweight infrared small target detection algorithm ISTD-YOLO based on improved YOLOv7 was proposed. Firstly, the YOLOv7 network structure was lightweight reconstructed, and a three-scale lightweight network architecture was designed. Then, the ELAN-W module of the model neck network is replaced by VoV-GSCSP to reduce the computational cost and the complexity of the network structure. Secondly, a parameter-free attention mechanism was introduced into the neck network to enhance the relevance of local con-text information. Finally, the Normalized Wasserstein Distance (NWD) was used to optimize the commonly used IoU index to enhance the localization and detection accuracy of small targets. Experimental results show that compared with YOLOv7 and the current mainstream algorithms, ISTD-YOLO can effectively improve the detection effect, and all indicators are effectively improved, which can achieve high-quality detection of infrared small targets.