Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2105.14974v1
• Date: Mon, 31 May 2021 14:04:58 GMT
• Title: Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
• Authors: Ting Liu, Jungang Yang, Boyang Li, Chao Xiao, Yang Sun, Yingqian Wang, Wei An
• Abstract summary: Infrared small target detection plays an important role in many infrared systems. Most low-rank methods assign different singular values to inaccurate background estimation. We propose a non-native spatial approximation (NTLA) for this small infrared target detection algorithm.
• Score: 32.67489082946838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.

Related papers

• Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection [15.605122893098981]
  This study introduces a Sparse Differential Directionality prior (SDD) framework. We leverage the distinct directional characteristics of targets to differentiate them from the background. We further enhance target detectability with a saliency coherence strategy. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model.
  arXiv  Detail & Related papers  (2024-07-22T04:32:43Z)
• 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)
• Infrared Small Target Detection Using Double-Weighted Multi-Granularity Patch Tensor Model With Tensor-Train Decomposition [6.517559383143804]
  This paper proposes a novel double-weighted multi-granularity infrared patch tensor (DWMGIPT) model. The proposed algorithm is robust to noise and different scenes.
  arXiv  Detail & Related papers  (2023-10-09T02:17:31Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• 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)
• Infrared small target detection based on isotropic constraint under complex background [10.091959130890956]
  Low signal-to-clutter ratio (SCR) of target and the interference caused by irregular background clutter make it difficult to get an accurate result. We propose a multilayer gray difference (MGD) method constrained by isotropy. Experiments show that the proposed method is effective and superior to several common methods in terms of signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve.
  arXiv  Detail & Related papers  (2020-11-24T12:25:05Z)
• Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection [121.28769542994664]
  Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier(RPC) demonstrate significantly different transferability when facing large domain gap.
  arXiv  Detail & Related papers  (2020-09-17T07:39:52Z)
• Effective Version Space Reduction for Convolutional Neural Networks [61.84773892603885]
  In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. We examine active learning with convolutional neural networks through the principled lens of version space reduction.
  arXiv  Detail & Related papers  (2020-06-22T17:40:03Z)
• A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box [10.689750889854269]
  A novel CNN-based ship detection method is proposed, by overcoming some common deficiencies of current CNN-based methods in ship detection. We are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network. Experimental results demonstrate the great superiority of the proposed method in ship detection.
  arXiv  Detail & Related papers  (2020-04-15T14:48:46Z)

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.