Real-time visual enhancement for infrared small dim targets in video

Sun, Xiaoliang; Liu, Xiaolin; Tang, Zhixuan; Long, Gucan; Yu, Qifeng

doi:10.1016/j.infrared.2017.05.002

Cited by 33 publications

(19 citation statements)

References 23 publications

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“…ompared to optical or radar detection, infrared detection has the advantages of all-day and all-weather operation, high resolution, and strong concealment simultaneously. Thus, the infrared detection systems have been widely used in air platform early warning and guidance [1]. Small infrared target detection is the key technology in such detection systems.…”

Section: Introductionmentioning

confidence: 99%

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Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Section: Introductionmentioning

confidence: 99%

“…successive frames is used in small infrared target detection. Accordingly, existing methods can be categorized into single frame based methods and multiple successive frames based methods [1]. Single frame based methods detect the small infrared target mainly using the differences between the target and background.…”

Section: Introductionmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The former group usually makes use of the assumption of the consistency information between target and background as well as the prior knowledge of the shape and motion characteristics of target, and then the target trajectory can be detected by the position correlation between successive frames with static background. The common methods include dynamic programming [33], [41], [46], particle filter [11], [25], [32], three-dimensional filter [21], [29], [30], projection transform [3], [24], [28], etc. However, in practice, the collected infrared images are in a dynamic background.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Xue

Shao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense-defense confrontation, etc. In this paper, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle (UAV) target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target and noise. Then, we put a nonconvex lowrank approximation on the background patch matrix to suppress the background edges, and put a reweighted l1,1 norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both l1,1 matrix norm and l2,1 matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.

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“…In the past decades, numerous researchers have made their contributions to this field and proposed some small target detection methods. As stated in [5]- [7], there are generally two kinds of small target detection methods, i.e., tracking before detecting (TBD) method [8], [9] and detecting before tracking (DBT) method [10], [11]. In one way, TBD methods will show better detecting and tracking performance than DBT methods since the TBD methods employ both temporal and spatial cues.…”

Section: Introductionmentioning

confidence: 99%

Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

Xia

Zhao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The traditional graph Laplacian model has been widely used in many computer vision tasks. The small target detection technique is one of the most challenging computer vision tasks in various practical applications. This paper presents a small target detection method by developing a modified graph Laplacian model with additional constraints. The proposed method is designed based on specific characteristics of small target: global rarity, local contrast, and contrast consistency. First, we analyze the primal graph Laplacian model and exploit its ability to describe global rarity and isolate outliers from nonoutliers. Next, indicators measuring local contrast and contrast consistency are constructed to delineate local characteristics of small targets. Then, we integrate the indicators with the primal graph Laplacian model and propose a modified graph Laplacian model for small target detection. In the confidence maps obtained by the proposed model, small targets are well enhanced, while backgrounds are significantly suppressed. Finally, a small target detection method is proposed based on the graph model. Extensive experiments on various real datasets demonstrate the effectiveness and superiority of the proposed method in detecting small targets.

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Real-time visual enhancement for infrared small dim targets in video

Cited by 33 publications

References 23 publications

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

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