You Only Look Omni Gradient Backpropagation for Moving Infrared Small Target Detection

• URL: http://arxiv.org/abs/2511.13013v1
• Date: Mon, 17 Nov 2025 06:13:41 GMT
• Title: You Only Look Omni Gradient Backpropagation for Moving Infrared Small Target Detection
• Authors: Guoyi Zhang, Guangsheng Xu, Siyang Chen, Han Wang, Xiaohu Zhang,
• Abstract summary: We propose BP-FPN, a backpropagation-driven feature pyramid architecture for small target detection.<n>The design is theoretically grounded, negligible computational introduces overhead, and can be seamlessly integrated into existing frameworks.<n>Extensive experiments on multiple public datasets show that BP-FPN consistently establishes new state-of-the-art performance.
• Score: 4.782962269769956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moving infrared small target detection is a key component of infrared search and tracking systems, yet it remains extremely challenging due to low signal-to-clutter ratios, severe target-background imbalance, and weak discriminative features. Existing deep learning methods primarily focus on spatio-temporal feature aggregation, but their gains are limited, revealing that the fundamental bottleneck lies in ambiguous per-frame feature representations rather than spatio-temporal modeling itself. Motivated by this insight, we propose BP-FPN, a backpropagation-driven feature pyramid architecture that fundamentally rethinks feature learning for small target. BP-FPN introduces Gradient-Isolated Low-Level Shortcut (GILS) to efficiently incorporate fine-grained target details without inducing shortcut learning, and Directional Gradient Regularization (DGR) to enforce hierarchical feature consistency during backpropagation. The design is theoretically grounded, introduces negligible computational overhead, and can be seamlessly integrated into existing frameworks. Extensive experiments on multiple public datasets show that BP-FPN consistently establishes new state-of-the-art performance. To the best of our knowledge, it is the first FPN designed for this task entirely from the backpropagation perspective.

Related papers

• Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection [22.128797773091403]
  Infrared small target detection (IRSTD) faces significant challenges due to the low signal-to-noise ratio (SNR), small target size, and complex cluttered backgrounds.<n>Recent DETR-based detectors exhibit notable performance degradation on IRSTD.
  arXiv  Detail & Related papers  (2026-01-06T09:14:01Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• Beyond Motion Cues and Structural Sparsity: Revisiting Small Moving Target Detection [5.375165101682048]
  Small moving target detection is crucial for many defense applications.<n>However, it remains highly challenging due to low signal-to-noise ratios, ambiguous visual cues, and cluttered backgrounds.<n>We propose a novel deep learning framework that differs fundamentally from existing approaches.
  arXiv  Detail & Related papers  (2025-09-09T12:20:25Z)
• Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better [63.567886330598945]
  Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, robust and efficient performance.<n>Current learning-based methods attempt to leverage more" information from both the spatial and the short-term temporal domains.<n>We propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection.
  arXiv  Detail & Related papers  (2025-06-15T08:19:32Z)
• It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations [5.326302374594885]
  In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet.<n>Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression-reconstruction-subtraction paradigm.<n>Experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency.
  arXiv  Detail & Related papers  (2025-06-12T07:24:45Z)
• 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)
• FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking [11.599952876425736]
  We propose a novel saliency-guided Siamese network tracker based on key fine-grained feature infor-mation.<n>This design captures essential global features from shallow layers, enhances feature diversity, and minimizes the loss of fine-grained in-formation.<n>Experiment results demonstrate that the pro-posed tracker achieves the highest precision and success rates.
  arXiv  Detail & Related papers  (2025-04-19T14:13:15Z)
• RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection [10.202639589226076]
  This work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks.
  arXiv  Detail & Related papers  (2023-11-02T01:21:12Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Centralized Feature Pyramid for Object Detection [53.501796194901964]
  Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. In this paper, we propose a OLO Feature Pyramid for object detection, which is based on a globally explicit centralized feature regulation.
  arXiv  Detail & Related papers  (2022-10-05T08:32:54Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)

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.