Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN

Peng, Chengyang; Jin, Shaohua; Bian, Gang; Cui, Yang; Wang, Meina

doi:10.3390/jmse12030467

Cited by 3 publications

(1 citation statement)

References 40 publications

(45 reference statements)

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“…The generated patch score maps, map_2, map_3, and map_4, provide more accurate feedback for the image's local regions, thereby enhancing the network's ability to recover detailed textures at different scales. This method is particularly effective in processing side-scan sonar images [38].…”

Section: Multi-scale Discriminator Structurementioning

confidence: 99%

SIGAN: A Multi-Scale Generative Adversarial Network for Underwater Sonar Image Super-Resolution

Peng,

Jin,

Bian

et al. 2024

JMSE

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Super-resolution (SR) is a technique that restores image details based on existing information, enhancing the resolution of images to prevent quality degradation. Despite significant achievements in deep-learning-based SR models, their application in underwater sonar scenarios is limited due to the lack of underwater sonar datasets and the difficulty in recovering texture details. To address these challenges, we propose a multi-scale generative adversarial network (SIGAN) for super-resolution reconstruction of underwater sonar images. The generator is built on a residual dense network (RDN), which extracts rich local features through densely connected convolutional layers. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to capture detailed texture information by focusing on different scales and channels. The discriminator employs a multi-scale discriminative structure, enhancing the detail perception of both generated and high-resolution (HR) images. Considering the increased noise in super-resolved sonar images, our loss function emphasizes the PSNR metric and incorporates the L2 loss function to improve the quality of the output images. Meanwhile, we constructed a dataset for side-scan sonar experiments (DNASI-I). We compared our method with the current state-of-the-art super-resolution image reconstruction methods on the public dataset KLSG-II and our self-built dataset DNASI-I. The experimental results show that at a scale factor of 4, the average PSNR value of our method was 3.5 higher than that of other methods, and the accuracy of target detection using the super-resolution reconstructed images can be improved to 91.4%. Through subjective qualitative comparison and objective quantitative analysis, we demonstrated the effectiveness and superiority of the proposed SIGAN in the super-resolution reconstruction of side-scan sonar images.

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Section: Multi-scale Discriminator Structurementioning

confidence: 99%

SIGAN: A Multi-Scale Generative Adversarial Network for Underwater Sonar Image Super-Resolution

Peng,

Jin,

Bian

et al. 2024

JMSE

Self Cite

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Consensus Control of Heterogeneous Uncertain Multiple Autonomous Underwater Vehicle Recovery Systems in Scenarios of Implicit Reduced Visibility

Li,

Zhang,

et al. 2024

JMSE

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This paper investigates consensus control in heterogeneous and uncertain multiple autonomous underwater vehicle (AUV) systems under implicit reduced visibility conditions. We address challenges such as environmental uncertainties and system nonlinearity by utilizing a unified connectivity approach to model low-visibility interactions and heterogeneous multi-AUV dynamics. Our main contributions include developing a feedback linearization model for heterogeneous multi-AUV systems that accounts for uncertainties, introducing an adaptive consensus controller based on relative positioning that effectively manages implicit visual interaction limitations and validating our strategies through stability analysis and numerical simulations. Our simulations demonstrate approximately a 60% improvement in accuracy compared to previous algorithms, highlighting the practical value of our approach in AUV recovery operations. These advancements provide a robust solution for consensus control in complex underwater environments.

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Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO

Ma,

Jin,

Bian

et al. 2024

Sensors

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Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets.

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Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN

Cited by 3 publications

References 40 publications

SIGAN: A Multi-Scale Generative Adversarial Network for Underwater Sonar Image Super-Resolution

SIGAN: A Multi-Scale Generative Adversarial Network for Underwater Sonar Image Super-Resolution

Consensus Control of Heterogeneous Uncertain Multiple Autonomous Underwater Vehicle Recovery Systems in Scenarios of Implicit Reduced Visibility

Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO

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