SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing

• URL: http://arxiv.org/abs/2507.09556v1
• Date: Sun, 13 Jul 2025 09:59:48 GMT
• Title: SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing
• Authors: Ximeng Zhai, Bohan Xu, Yaohong Chen, Hao Wang, Kehua Guo, Yimian Dai,
• Abstract summary: We propose a novel task, Sequential CSIST Unmixing, for detecting all targets in the form of sub-pixel localization from a highly dense CSIST group.<n>We contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task.<n>Our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3%.
• Score: 7.09321729956876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as mixing spots in the infrared image. In this paper, we propose a novel task, Sequential CSIST Unmixing, namely detecting all targets in the form of sub-pixel localization from a highly dense CSIST group. However, achieving such precise detection is an extremely difficult challenge. In addition, the lack of high-quality public datasets has also restricted the research progress. To this end, firstly, we contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task, along with the implementation of 23 relevant methods. Furthermore, we propose the Deformable Refinement Network (DeRefNet), a model-driven deep learning framework that introduces a Temporal Deformable Feature Alignment (TDFA) module enabling adaptive inter-frame information aggregation. To the best of our knowledge, this work is the first endeavor to address the CSIST Unmixing task within a multi-frame paradigm. Experiments on the SeqCSIST dataset demonstrate that our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3\%. Our dataset and toolkit are available from https://github.com/GrokCV/SeqCSIST.

Related papers

• SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection [12.964308630328688]
  Infrared small target detection (ISTD) is vital for long-range surveillance in military, maritime, and early warning applications.<n>ISTD is challenged by targets occupying less than 0.15% of the image and low distinguishability from complex backgrounds.<n>This paper presents SAMamba, a novel framework integrating SAM2's hierarchical feature learning with Mamba's selective sequence modeling.
  arXiv  Detail & Related papers  (2025-05-29T07:55:23Z)
• DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing [55.366556355538954]
  We propose the Dynamic Iterative Shrinkage Thresholding Network (DISTA-Net), which reconceptualizes traditional sparse reconstruction within a dynamic framework.<n>DISTA-Net is the first deep learning model designed specifically for the unmixing of closely-spaced infrared small targets.<n>We have established the first open-source ecosystem to foster further research in this field.
  arXiv  Detail & Related papers  (2025-05-25T13:52:00Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [58.67129770371016]
  We propose a novel IRSTD framework that reimagines the IRSTD paradigm by incorporating textual metadata for scene-aware optimization.<n>AuxDet consistently outperforms state-of-the-art methods, validating the critical role of auxiliary information in improving robustness and accuracy.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection [4.731073701194089]
  Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications.<n>Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments.<n>We propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD.
  arXiv  Detail & Related papers  (2025-05-15T03:56:36Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection [48.707233614642796]
  Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data. We propose the first method to achieve SIRST detection with single-point supervision.
  arXiv  Detail & Related papers  (2023-04-10T08:04:05Z)
• One-Stage Cascade Refinement Networks for Infrared Small Target Detection [21.28595135499812]
  Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics. We present a new research benchmark for infrared small target detection consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets.
  arXiv  Detail & Related papers  (2022-12-16T13:37:23Z)
• Efficient Person Search: An Anchor-Free Approach [86.45858994806471]
  Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. In this work, we present an anchor-free approach to efficiently tackling this challenging task, by introducing the following dedicated designs.
  arXiv  Detail & Related papers  (2021-09-01T07:01:33Z)
• Adaptive Linear Span Network for Object Skeleton Detection [56.78705071830965]
  We propose adaptive linear span network (AdaLSN) to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction.
  arXiv  Detail & Related papers  (2020-11-08T12:51:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.