An application of automatic target recognition in marine navigation

Alexandrov, Chavdar; Draganov, Andrew; Kolev, Nikolai

doi:10.1109/radar.1995.522554

Cited by 16 publications

(3 citation statements)

References 5 publications

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“…When the convolution kernel is fused, as shown in Figure 3, each branch's convolution layer is fused with the subsequent BN layer. Let 𝑥 𝑖 represent the i-th channel of the input feature, and 𝜇 𝑖 and 𝜎 𝑖 2 represent the corresponding mean and variance, respectively, then the mathematical description of BN can be expressed as Equation (1).…”

Section: Rsammentioning

confidence: 99%

“…As a result, accurate identification and positioning of vessels are critical to ensuring public safety in the maritime strait area and the long-term sustainability of the marine shipping sector. A radar system is a typical and successful technical way of identifying and locating vessels at sea [1,2,3]. Following the transmission of microwave pulses, radar equipment processes the echoes reflected from the target by receiving equipment and can perform additional operations based on this information.…”

Section: Introductionmentioning

confidence: 99%

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Vessel classification algorithm based on the convolutional attention module for natural maritime images

Zhao¹,

Zheng²,

Zhang³

et al. 2023

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

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In recent years, to ensure the stable development of the marine economy, the analysis and processing of maritime vessel targets combined with natural light images have played a significant role. However, in scenes such as ports, there are sometimes background noises similar to vessel features, so the classifier based on the convolutional neural network cannot achieve good classification results. To pay more attention to effective information when classifying natural maritime images, we propose an efficient channel attention module (ECAM) and a re-parameterized spatial attention module (RSAM) through one-dimensional convolution and re-parameterization method. This paper combines ECAM and RSAM to present an efficient re-parameterized convolutional block attention module (ER-CBAM) to classify natural maritime images. Besides, in response to the current lack of large-scale natural marine image datasets, we established a natural maritime vessel image (NMVI) dataset. Experiments on NMVI show that by combining the proposed attention module, ResNet50 can achieve a top-1 accuracy score of 85.32% with almost no extra computational consumption and a 3.02% improvement compared to the previous 82.30%, which suggests that the proposed method is suitable for maritime scenarios.

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Section: Rsammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vessel classification algorithm based on the convolutional attention module for natural maritime images

Zhao¹,

Zheng²,

Zhang³

et al. 2023

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

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show abstract

“…Target length is an obvious feature to extract and has been investigated for low resolution radars in [3] [5]. It is Fifth International Symposium on Signal Processing and 'its Applications, ISSPA '99, Brisbane, Australia, 22-25 August, 1999 Organised by the Signal Processing Research Centre, QUT, Brisbane, Australia Averagelength(m) Standard Deviation (m) estimated using the start point, end point, known pixel resolution and orientation of the target.…”

Section: Length Estimationmentioning

confidence: 99%

Features for high resolution radar range profile based ship classification

Slomka

Gibbins²,

Gray³

et al.

ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and Its Applications (IEEE Cat. No.99EX359)

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This study investigates a variety of features in the context of automated ship classification of high resolution radar range profile. The features used are length, scatterer count, centres of mass, quantised range profile and Fourier Modified Direct Mellin Tranform coefficients. The results of evaluation using a modest database of high resolution range profiles, collected using an airborne radar, are then presented.

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Active and Passive Radar Target Fusion Recognition Method Based on Bayesian Network

Zhang

Sun

2022

2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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An application of automatic target recognition in marine navigation

Cited by 16 publications

References 5 publications

Vessel classification algorithm based on the convolutional attention module for natural maritime images

Vessel classification algorithm based on the convolutional attention module for natural maritime images

Features for high resolution radar range profile based ship classification

Active and Passive Radar Target Fusion Recognition Method Based on Bayesian Network

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