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Link to original content: https://unpaywall.org/10.1007/S10489-022-03683-1
Small-target ship detection in SAR images based on densely connected deep neural network with attention in complex scenes | Applied Intelligence Skip to main content
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Small-target ship detection in SAR images based on densely connected deep neural network with attention in complex scenes

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Abstract

Ship detection research has made great progress with deep learning method in recent years. Because some ship targets are too small and the complex scenes are difficult to distinguish from the ship, the accuracy of small-target ship detection in synthetic aperture radar (SAR) images still requires improvement. In order to effectively reduce the interference of complex scenes and locate the small-target more precisely, we propose a model (Dense-YOLOv4-CBAM) based on a densely connected deep neural network with an attention mechanism. The main improvements and contributions of our propose model are three aspects. First, the dense connections is increased in the backbone network (CSP-Darknet53) based on the YOLOv4 framework to enhance the transmission of image features. Second, to refine the transmission of top-down semantic features and bottom-up positioning features in the feature fusion module, we notivatively insert a spatial and channel convolutional attention mechanism (CBAM) into the feature fusion module. This mechanism can reduce the interference in complex scenes and enable a more effective use of each layer’s features to reduce the loss of the semantic information of small targets. Finally, we introduce two kinds of accuracy evaluation metrics to evaluate the effectiveness and robustness of the proposed method in extensive experiments. The results show that the proposed model achieves an optimal performance between the accuracy and calculation time compared to other state-of-the-art detection methods on public synthetic aperture radar ship detection datasets.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Bowen Sun, Xiaofeng Wang & Yang Wang

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Hao Li

  3. College of Information Engineering, Shaoyang University, Shaoyang, 422000, China

    Feng Dong

Authors
  1. Bowen Sun
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  2. Xiaofeng Wang
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  3. Hao Li
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  4. Feng Dong
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  5. Yang Wang
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Correspondence to Bowen Sun.

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Sun, B., Wang, X., Li, H. et al. Small-target ship detection in SAR images based on densely connected deep neural network with attention in complex scenes. Appl Intell 53, 4162–4179 (2023). https://doi.org/10.1007/s10489-022-03683-1

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