DIE-CDK: A Discriminative Information Enhancement Method With Cross-Modal Domain Knowledge for Fine-Grained Ship Detection

Guo, Yinsai; Yu, Hang; Ma, Liyan; Luo, Xiangfeng; Xie, Shaorong

doi:10.1109/tcsvt.2024.3407057

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2024

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Cited by 2 publications

References 51 publications

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Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

Zhang,

et al. 2024

Lecture Notes in Computer Science

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Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

Zhang,

et al. 2024

Lecture Notes in Computer Science

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Ship detection based on semantic aggregation for video surveillance images with complex backgrounds

Ren,

Liu,

Yang

et al. 2024

PeerJ Computer Science

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Background Ship detection in video surveillance images holds significant practical value. However, the background in these images is often complex, complicating the achievement of an optimal balance between detection precision and speed. Method This study proposes a ship detection method that leverages semantic aggregation in complex backgrounds. Initially, a semantic aggregation module merges deep features, rich in semantic information, with shallow features abundant in location details, extracted via the front-end network. Concurrently, these shallow features are reshaped through the reorg layer to extract richer feature information, and then these reshaped shallow features are integrated with deep features within the feature fusion module, thereby enhancing the capability for feature fusion and improving classification and positioning capability. Subsequently, a multiscale object detection layer is implemented to enhance feature expression and effectively identify ship objects across various scales. Moreover, the distance intersection over union (DIoU) metric is utilized to refine the loss function, enhancing the detection precision for ship objects. Results The experimental results on the SeaShips dataset and SeaShips_enlarge dataset demonstrate that the mean average precision@0.5 (mAP@0.5) of this proposed method reaches 89.30% and 89.10%, respectively. Conclusions The proposed method surpasses other existing ship detection techniques in terms of detection effect and meets real-time detection requirements, underscoring its engineering relevance.

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DIE-CDK: A Discriminative Information Enhancement Method With Cross-Modal Domain Knowledge for Fine-Grained Ship Detection

Cited by 2 publications

References 51 publications

Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

Ship detection based on semantic aggregation for video surveillance images with complex backgrounds

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