Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2311.08747v3
• Date: Wed, 17 Jan 2024 07:47:29 GMT
• Title: Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
• Authors: Chun Bao, Jie Cao, Yaqian Ning, Tianhua Zhao, Zhijun Li, Zechen Wang, Li Zhang, and Qun Hao
• Abstract summary: 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.
• Score: 8.388564430699155
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Infrared small target detection based on deep learning offers unique advantages in separating small targets from complex and dynamic backgrounds. However, the features of infrared small targets gradually weaken as the depth of convolutional neural network (CNN) increases. To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture. We preserve the dense nested structure of dense nested attention network (DNANet) and introduce the Swin-transformer during feature extraction stage to enhance the continuity of features. Furthermore, we integrate the ACmix attention structure into the dense nested structure to enhance the features of intermediate layers. Additionally, we design a weighted dice binary cross-entropy (WD-BCE) loss function to mitigate the negative impact of foreground-background imbalance in the samples. Moreover, we develop a dataset specifically for infrared small targets, called BIT-SIRST. The dataset comprises a significant amount of real-world targets and manually annotated labels, as well as synthetic data and corresponding labels. We have evaluated the effectiveness of our method through experiments conducted on public datasets. In comparison to other state-of-the-art methods, our approach outperforms in terms of probability of detection ($P_d$), false-alarm rate ($F_a$), and mean intersection of union ($mIoU$). The $mIoU$ reaches 90.89\% on the NUDT-SIRST dataset and 79.72\% on the SIRST dataset. The BIT-SIRST dataset and codes are available openly at \href{https://github.com/EdwardBao1006/bit\_sirst}{\color[HTML]{B22222}{https://github.com/EdwardBao1006/bit\_sirst}}.

Related papers

• 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)
• EFLNet: Enhancing Feature Learning for Infrared Small Target Detection [20.546186772828555]
  Single-frame infrared small target detection is considered to be a challenging task. Due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small target. We propose an enhancing feature learning network (EFLNet) to address these problems.
  arXiv  Detail & Related papers  (2023-07-27T09:23:22Z)
• 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)
• Provable Data Subset Selection For Efficient Neural Network Training [73.34254513162898]
  We introduce the first algorithm to construct coresets for emphRBFNNs, i.e., small weighted subsets that approximate the loss of the input data on any radial basis function network. We then perform empirical evaluations on function approximation and dataset subset selection on popular network architectures and data sets.
  arXiv  Detail & Related papers  (2023-03-09T10:08:34Z)
• SAR Despeckling Using Overcomplete Convolutional Networks [53.99620005035804]
  despeckling is an important problem in remote sensing as speckle degrades SAR images. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
  arXiv  Detail & Related papers  (2022-05-31T15:55:37Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)
• 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)
• Dense Nested Attention Network for Infrared Small Target Detection [36.654692765557726]
  Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. Existing CNN-based methods cannot be directly applied for infrared small targets. We propose a dense nested attention network (DNANet) in this paper.
  arXiv  Detail & Related papers  (2021-06-01T13:45:35Z)
• MobileSal: Extremely Efficient RGB-D Salient Object Detection [62.04876251927581]
  This paper introduces a novel network, methodname, which focuses on efficient RGB-D salient object detection (SOD) We propose an implicit depth restoration (IDR) technique to strengthen the feature representation capability of mobile networks for RGB-D SOD. With IDR and CPR incorporated, methodnameperforms favorably against sArt methods on seven challenging RGB-D SOD datasets.
  arXiv  Detail & Related papers  (2020-12-24T04:36:42Z)
• Attentional Local Contrast Networks for Infrared Small Target Detection [15.882749652217653]
  We propose a novel model-driven deep network for infrared small target detection. We modularize a conventional local contrast measure method as a depth-wise parameterless nonlinear feature refinement layer in an end-to-end network. We conduct detailed ablation studies with varying network depths to empirically verify the effectiveness and efficiency of each component in our network architecture.
  arXiv  Detail & Related papers  (2020-12-15T19:33:09Z)

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.