An Adaptation of Cnn for Small Target Detection in the Infrared

Zhao, Dong; Zhou, Huixin; Rang, Shenghui; Jia, Xiuping

doi:10.1109/igarss.2018.8518464

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…ISOS methods can generally be categorized into two groups, depending on whether they are CNN-based or not. Recently, fully connected network (FCN) [28] based methods have been applied to ISOS [7,22,[29][30][31]. In particular, DataLossGAN [22] and Asymmetric Contextual Modulation (ACM) [31] have obtained magnificent performance on their datasets.…”

Section: Related Workmentioning

confidence: 99%

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AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Song

Kim

2021

Remote Sensing

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Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).

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Section: Related Workmentioning

confidence: 99%

“…In comparison, the CNN-based methods [7,22,[29][30][31] have numerous filters, which are sufficient to cover the distribution of all the test data. This is why the CNN is superior in terms of absolute performance measure, than the handcraft-based methods.…”

Section: Deeper Network Vs Vanishing Gradientmentioning

confidence: 99%

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Song

Kim

2021

Remote Sensing

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“…However, they become less effective when encountering dim small target scenes, resulting in missing targets with high probability. Recently, deep convolutional neural networks have been employed for the community of small target detection [41][42][43][44]. Lin et al [42] designed a seven-layer conventional neural network in an end-to-end way to automatically extract small target features and eliminate clutter.…”

Section: Related Algorithmsmentioning

confidence: 99%

“…Lin et al [42] designed a seven-layer conventional neural network in an end-to-end way to automatically extract small target features and eliminate clutter. With the help of massive training samples generation, Zhao et al [43] suggested a simple conventional network for modeling the background patches. Such methods show good robustness even in some complex situations with heavy clutter but they require a great quantity of labeled training data, which may not always be available in practice.…”

Section: Related Algorithmsmentioning

confidence: 99%

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Zhou

Dai

et al. 2020

Remote Sensing

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Small target detection is a critical step in remotely infrared searching and guiding applications. However, previously proposed algorithms would exhibit performance deterioration in the presence of complex background. It is attributed to two main reasons. First, some common background interferences are difficult to eliminate effectively by using conventional sparse measure. Second, most methods only exploit the spatial information typically, but ignore the structural priors across feature space. To address these issues, this paper gives a novel model combining the spatial-feature graph regularization and l1/2-norm sparse constraint. In this model, the spatial and feature regularizations are imposed on the sparse component in the form of graph Laplacians, where the sparse component is enforced as the eigenvectors of their graph Laplacian matrices. Such an approach is to explore the geometric information in both data and feature space simultaneously. Moreover, l1/2-norm acts as a substitute of the traditional l1-norm to constrain the sparse component, further reducing the fake targets. Finally, an efficient optimization algorithm equipped with linearized alternating direction method with adaptive penalty (LADMAP) is carefully designed for model solution. Comprehensive experiments on different infrared scenes substantiate the superiority of the proposed method beyond 11 competitive algorithms in subjective and objective evaluation.

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“…Researchers have exerted considerable efforts in the past decade, but infrared small target detection is still a challenging task worth exploring [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Yan

Xin

et al. 2020

IEEE Access

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Infrared small target detection is a crucial and challenging topic for various applications. In recent years, the spectrum scale space (SSS) algorithm has shown considerable potential in the field of target detection. However, the SSS algorithm is prone to high false alarm rates in infrared small target detection scenarios with complex background. This paper proposes an improved SSS (ISSS) algorithm via precise feature matching and scale selection strategy for efficient infrared small target detection, which includes background suppression, feature matching and optimal scale selection three stages. In the background suppression stage, a matrix decomposition method named inexact augmented Lagrange multiplier (IALM) algorithm is used to extract the sparse image matrix from the original image as the target foreground image. In the feature matching stage, the 16 elaborate Gaussian kernel functions convolve with the the amplitude spectrum of target foreground image to generate 16 scale saliency maps that precisely match the feature of small targets. In the optimal scale selection stage, a few proper candidate scale maps are screened out according to the difference between the pixel values of the target area and the background clutters, in which the target area was more highlighted, and the scale map corresponding to the maximum value of local information entropy of the candidate saliency map is the final detection result map. We mainly made three contributions: First, IALM algorithm is utilized as a preprocessing step, and we have verified it is indispensable in eliminating most backgrounds with self-correlation property. Second, an elaborate scale division strategy is proposed to obtain multi-scale saliency maps that match the feature of infrared small targets precisely. Third, the gray value difference and the maximum value of local information entropy are defined and used as the judgment criteria for optimal scale selection. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art techniques, especially on infrared images with thick clouds and high-brightness buildings. INDEX TERMS Infrared small target detection, improved spectrum scale space, matrix decomposition.

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An Adaptation of Cnn for Small Target Detection in the Infrared

Cited by 12 publications

References 11 publications

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

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