Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network

Wang, Yun; Shi, Hao; Chen, Liang

doi:10.1007/s12524-022-01491-1

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…For instance, Chen et al [8] made several improvements to Faster R-CNN in multiple aspects, including optimizing the backbone network, sample data balancing, and scale normalization, aiming to enhance the algorithm's accuracy and robustness. Additionally, differential neural architecture search, label reassignment, and various types of feature pyramid structures have been widely applied in algorithm design, yielding promising recognition results [9][10][11]. In conclusion, deep neural networks have proven effective in extracting ship features.…”

Section: Ship Identification Methods Under Radar and Other Scenariosmentioning

confidence: 99%

MrisNet: Robust Ship Instance Segmentation in Challenging Marine Radar Environments

Ma,

Kang,

Chen

et al. 2023

JMSE

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In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

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Section: Ship Identification Methods Under Radar and Other Scenariosmentioning

confidence: 99%

MrisNet: Robust Ship Instance Segmentation in Challenging Marine Radar Environments

Ma,

Kang,

Chen

et al. 2023

JMSE

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show abstract

“…Each of these ship categories presents its own set of features and structural complexities, rendering the classification task more challenging than initially perceived. The distinctiveness of these ships in terms of size, structural design, and functionalities inevitably introduces a high degree of intra-class variability [29,30].…”

Section: Proposed Heterogeneous Ship Datamentioning

confidence: 99%

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Tienin,

Cui,

Mba Esidang

et al. 2023

Remote Sensing

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The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.

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Ship detection based on deep learning using SAR imagery: a systematic literature review

Yasir

Wan

et al. 2022

Soft Comput

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Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network

Cited by 6 publications

References 23 publications

MrisNet: Robust Ship Instance Segmentation in Challenging Marine Radar Environments

MrisNet: Robust Ship Instance Segmentation in Challenging Marine Radar Environments

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Ship detection based on deep learning using SAR imagery: a systematic literature review

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