Small Ship Detection of SAR Images Based on Optimized Feature Pyramid and Sample Augmentation

Gong, Yicheng; Zhang, Zhuo; Wen, Jiabao; Lan, Guipeng; Xiao, Shuai

doi:10.1109/jstars.2023.3302575

Cited by 22 publications

(1 citation statement)

References 37 publications

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“…Zhou et al [39] effectively integrated information from various oriented gradients using multi-scale convolutions, thus extracting more robust features. Gong et al [40] proposed SSPNet for small ship detection, where they employed context attention and scale-enhanced attention mechanisms to make the model more attentive to small-scale targets. Qian et al [41], by combining the deep Long Short-Term Memory network (LSTM) with genetic algorithms (GA), proposed the GA-LSTM model, significantly enhancing the accuracy and speed of trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

Huang,

Liu,

Chen

2023

Remote Sensing

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Detecting small ship targets in large-scale synthetic aperture radar (SAR) images with complex backgrounds is challenging. This difficulty arises due to indistinct visual features and noise interference. To address these issues, we propose a novel two-stage detector, namely a convolutional and visual transformer fusion network (CViTF-Net), and enhance its detection performance through three innovative modules. Firstly, we designed a pyramid structured CViT backbone. This design leverages convolutional blocks to extract low-level and local features, while utilizing transformer blocks to capture inter-object dependencies over larger image regions. As a result, the CViT backbone adeptly integrates local and global information to bolster the feature representation capacity of targets. Subsequently, we proposed the Gaussian prior discrepancy (GPD) assigner. This assigner employs the discrepancy of Gaussian distributions in two dimensions to assess the degree of matching between priors and ground truth values, thus refining the discriminative criteria for positive and negative samples. Lastly, we designed the level synchronized attention mechanism (LSAM). This mechanism simultaneously considers information from multiple layers in region of interest (RoI) feature maps, and adaptively adjusts the weights of diverse regions within the final RoI. As a result, it enhances the capability to capture both target details and contextual information. We achieved the highest comprehensive evaluation results for the public LS-SSDD-v1.0 dataset, with an mAP of 79.7% and an F1 of 80.8%. In addition, the robustness of the CViTF-Net was validated using the public SSDD dataset. Visualization of the experimental results indicated that CViTF-Net can effectively enhance the detection performance for small ship targets in complex scenes.

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Section: Introductionmentioning

confidence: 99%

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

Huang,

Liu,

Chen

2023

Remote Sensing

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FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement

Liu,

Yan

2024

Machine Vision and Applications

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Synthetic Aperture Radar (SAR) plays a crucial role in ship monitoring due to its allweather and high-resolution capabilities. In SAR images, ship targets often exhibit blurred or mixed boundaries with the background, and there may be occlusion or partial occlusion. Furthermore, the multi-scale transformation and the presence of small targets pose challenges to ship detection. To address these challenges, a novel SAR ship detection model, FESar, is proposed. First, to address the problem of large-scale transformations in ship detection, a network FCEM with fused convolutional enhancement is proposed, in which different convolutional branches are designed to capture local and global features, respectively, and are fused and enhanced. Secondly, an FPE module containing a spatial-mixing layer is designed to capture and analyze local spatial relationships in the image, and effectively combine local information to discriminate the feature information between ship targets and the background. Finally, a new backbone network, SPD-YOLO, is designed to perform deep downsampling for the comprehensive extraction of semantic information related to ships. To validate the performance of the model, experiments are conducted on the publicly available dataset LS-SSSDD-v1.0, and the experimental results show that the performance of the proposed FESar model outperforms many SOTA models, and based on the base model, FESar improves the AP by 5.5% on the dataset LS-SSDD-v1.0. Compared with the SAR ship detection model on the SSDD dataset, the comprehensive performance of FESAR is better than other SAR ship detection models. To verify the generalization of the model, we experiment with FESAR with numerous SOTA models on the dataset HRSID, and the experimental results show that, based on the base model, the FESAR model improves AP by 2.6% on the dataset HRSID.

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Image Quality Assessment via Inter-class and Intra-class Differences for Efficient Classification

Yang,

et al. 2023

Neural Process Lett

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Small Ship Detection of SAR Images Based on Optimized Feature Pyramid and Sample Augmentation

Cited by 22 publications

References 37 publications

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement

Image Quality Assessment via Inter-class and Intra-class Differences for Efficient Classification

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