SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection

• URL: http://arxiv.org/abs/2403.05416v1
• Date: Fri, 8 Mar 2024 16:14:54 GMT
• Title: SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection
• Authors: Yahao Lu, Yupei Lin, Han Wu, Xiaoyu Xian, Yukai Shi, Liang Lin
• Abstract summary: 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.
• Score: 53.19618419772467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has not been improved accordingly. The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets. To highlight this issue, we propose a negative sample augmentation method in this paper. Specifically, a negative augmentation approach is proposed to generate massive negatives for self-supervised learning. Firstly, we perform a sequential noise modeling technology to generate realistic infrared data. Secondly, we fuse the extracted noise with the original data to facilitate diversity and fidelity in the generated data. Lastly, we proposed a negative augmentation strategy to enrich diversity as well as maintain semantic invariance. The proposed algorithm produces a synthetic SIRST-5K dataset, which contains massive pseudo-data and corresponding labels. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed. Compared with other state-of-the-art (SOTA) methods, our method achieves outstanding performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection over union (IoU).

Related papers

• Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection [57.666055329221194]
  We investigate the adaptation of generic segmentation models, such as the Segment Anything Model (SAM), to infrared small object detection tasks. Our model demonstrates significantly improved performance in both accuracy and throughput compared to existing approaches.
  arXiv  Detail & Related papers  (2024-09-07T05:31:24Z)
• Better Sampling, towards Better End-to-end Small Object Detection [7.7473020808686694]
  Small object detection remains unsatisfactory due to limited characteristics and high density and mutual overlap. We propose methods enhancing sampling within an end-to-end framework. Our model demonstrates a significant enhancement, achieving a 2.9% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset.
  arXiv  Detail & Related papers  (2024-05-17T04:37:44Z)
• Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling [5.248337726304453]
  Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background. We propose a diffusion model framework for Infrared Small Target Detection which compensates pixel-level discriminant with mask posterior distribution modeling. Experiments show that the proposed method achieves competitive performance gains over state-of-the-art methods on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets.
  arXiv  Detail & Related papers  (2024-03-13T09:45:30Z)
• SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector [60.42293239557962]
  We propose SpirDet, a novel approach for efficient detection of infrared small targets. We employ a new dual-branch sparse decoder to restore the feature map. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-02-08T05:06:14Z)
• Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection [8.388564430699155]
  Infrared small target detection based on deep learning offers unique advantages in separating small targets from complex and dynamic backgrounds. The features of infrared small targets gradually weaken as the depth of convolutional neural network (CNN) increases. We propose improved dense nested attention network (IDNANet), which is based on the transformer architecture.
  arXiv  Detail & Related papers  (2023-11-15T07:29:24Z)
• Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection [22.60228799622782]
  Key bottleneck in object detection in IR images is lack of sufficient labeled training data. We seek to leverage cues from the RGB modality to scale object detectors to the IR modality, while preserving model performance in the RGB modality. We first pretrain these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality to avoid over-fitting.
  arXiv  Detail & Related papers  (2023-09-28T16:55:52Z)
• ABC: Attention with Bilinear Correlation for Infrared Small Target Detection [4.7379300868029395]
  CNN based deep learning methods are not effective at segmenting infrared small target (IRST) We propose a new model called attention with bilinear correlation (ABC) ABC is based on the transformer architecture and includes a convolution linear fusion transformer (CLFT) module with a novel attention mechanism for feature extraction and fusion.
  arXiv  Detail & Related papers  (2023-03-18T03:47:06Z)
• ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data [93.06336507035486]
  Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. We present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks.
  arXiv  Detail & Related papers  (2022-10-27T02:55:15Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection [145.4919781325014]
  How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection. Many models use the feature fusion strategy but are limited by the low-order point-to-point fusion methods. We propose a novel mutual attention model by fusing attention and contexts from different modalities.
  arXiv  Detail & Related papers  (2020-10-12T08:50:10Z)

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.