Infrared target detection using kernel Rayleigh quotient quadratic correlation filter

Wu, Yanru; Chen, Yongmei; Zhao, Yue; Gao, Shibo

doi:10.3724/sp.j.1010.2011.00142

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The combination of many factors results in a very low signal-to-noise ratio (SNR) of the image in the actual complex scene [1]. Because the grayscale of the background region is close to the target region, or even stronger than the target region under certain background conditions, the detection process is lack of significant features and texture information [2][3][4]. Therefore, it is still necessary to conduct indepth research on the use of infrared imaging systems for long-range detection of the aerial target [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A fast target detection method in the sky background

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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Infrared imaging technology has unique advantages in the field of aerial motion target detection, but usually the information captured by the target is limited. Because the small target is easy to be submerged in the background, the existing method is difficult to detect the small target quickly and accurately under various complex environments. This paper proposes a fast detection method by using the morphological features of targets in infrared images. This method increases the morphological differences between target and non-target regions in the image through high pass filtering, and then uses the SUSAN edge detection operator to scan the processed image and detect the target interest region. In MATLAB simulation environment, the effectiveness of the algorithm was verified through infrared image sequences captured in specific actual sky background. Through experimental data obtained under specific conditions, it has been proven that this method can detect targets and overcome the influence of complex interference when the sample data is limited.

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Section: Introductionmentioning

confidence: 99%

A fast target detection method in the sky background

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…To overcome these problems, many infrared small target detection methods based on deep learning are proposed, which can achieve better detection performance due to their more powerful feature representation ability and stronger generalization ability. Wu et al [11] transform the detection problem of infrared small targets into a classification network to solve the location distribution of small targets. Dai et al [12] propose an asymmetric contextual modulation approach to fuse bidirectional global contextual interaction information to obtain a more efficient localization performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale feature fusion attention network for infrared small target detection

Zhang

Liu

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Compared with other target detection tasks, infrared small target detection has the problem of feature information loss in deep networks due to fewer target pixels and the lack of color and texture features. To address aforementioned issue, a Multi-Scale Feature Fusion Attention Network (MSFFA) is proposed to better utilize shallow edge features and deep semantic features. Its main components contain Convolutional Block Attention Module (CBAM), Multi-Scale Receptive Field Feature Fusion Module (R3FM), and Bidirectional Feature Aggregation Network (BFANet). CBAM is designed to calculate the importance of each feature map and enhance useful features from the channel and spatial dimensions. R3FM is proposed to characterize the global context information of deep layers feature map to enlarge the network's receptive field for small targets detection with a larger range of location information. BFANet is developed to shorten the path of information exchange between different layers and reinforce the utilization of shallow features in the network. Moreover, the K-means clustering algorithm is adopted to optimize the width to height ratio of the bounding anchor, and it can better match the positive samples to improve the training performance. Extensive experiments on public infrared small target detection dataset demonstrate that the proposed method achieves better performance compared to the other state-of-the-art methods.

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Infrared target detection using kernel Rayleigh quotient quadratic correlation filter

Cited by 2 publications

References 10 publications

A fast target detection method in the sky background

A fast target detection method in the sky background

Multi-scale feature fusion attention network for infrared small target detection

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