MPANet: Multi-Patch Attention For Infrared Small Target object Detection
- URL: http://arxiv.org/abs/2206.02120v1
- Date: Sun, 5 Jun 2022 08:01:38 GMT
- Title: MPANet: Multi-Patch Attention For Infrared Small Target object Detection
- Authors: Ao Wang, Wei Li, Xin Wu, Zhanchao Huang, and Ran Tao
- Abstract summary: Infrared small target detection (ISTD) has attracted widespread attention and been applied in various fields.
Due to the small size of infrared targets and the noise interference from complex backgrounds, the performance of ISTD using convolutional neural networks (CNNs) is restricted.
A multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed.
- Score: 11.437699171778544
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Infrared small target detection (ISTD) has attracted widespread attention and
been applied in various fields. Due to the small size of infrared targets and
the noise interference from complex backgrounds, the performance of ISTD using
convolutional neural networks (CNNs) is restricted. Moreover, the constriant
that long-distance dependent features can not be encoded by the vanilla CNNs
also impairs the robustness of capturing targets' shapes and locations in
complex scenarios. To this end, a multi-patch attention network (MPANet) based
on the axial-attention encoder and the multi-scale patch branch (MSPB)
structure is proposed. Specially, an axial-attention-improved encoder
architecture is designed to highlight the effective features of small targets
and suppress background noises. Furthermore, the developed MSPB structure fuses
the coarse-grained and fine-grained features from different semantic scales.
Extensive experiments on the SIRST dataset show the superiority performance and
effectiveness of the proposed MPANet compared to the state-of-the-art methods.
Related papers
- Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction.
Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features.
RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
arXiv Detail & Related papers (2024-09-09T13:56:22Z) - Infrared Small Target Detection based on Adjustable Sensitivity Strategy and Multi-Scale Fusion [2.661766509317245]
We propose a refined infrared small target detection scheme based on an adjustable sensitivity (AS) strategy and multi-scale fusion.
Specifically, a multi-scale model fusion framework based on multi-scale direction-aware network (MSDA-Net) is constructed.
This scheme won the first prize in the PRCV 2024 wide-area infrared small target detection competition.
arXiv Detail & Related papers (2024-07-29T15:22:02Z) - Multi-Scale Direction-Aware Network for Infrared Small Target Detection [2.661766509317245]
Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target.
We propose a multi-scale direction-aware network (MSDA-Net) to integrate the high-frequency directional features of infrared small targets.
MSDA-Net achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST and IRSTD-1k datasets.
arXiv Detail & Related papers (2024-06-04T07:23:09Z) - 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) - 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) - RRNet: Relational Reasoning Network with Parallel Multi-scale Attention
for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.
We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs.
Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
arXiv Detail & Related papers (2021-10-27T07:18:32Z) - 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) - 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) - Depthwise Non-local Module for Fast Salient Object Detection Using a
Single Thread [136.2224792151324]
We propose a new deep learning algorithm for fast salient object detection.
The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
arXiv Detail & Related papers (2020-01-22T15:23:48Z)
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.