Search

Although ship detection methods based on deep learning have achieved remarkable progress, the design of the universal ship detection (USD) system is rarely studied. The main challenge of USD lies in the notorious domain bias and shift problem across multiple domains. This article implements USD based on domain-invariant representations to alleviate this issue. Specifically, the proposed method integrates a multilevel domain classification network (MDCN) and a domain-centric cut-paste module (DCM). First, the backbone network is facilitated to learn domain-independent image features from multiple domains through MDCN, thereby reducing the disturbance of domain-specific features to universal detector. Furthermore, the proposed method combines the domain-related synthetic samples generated by DCM to provide MDCN with strong supervision information, which further motivates the network to be more attentive to the domain-invariant representations at the instance level. Finally, we conduct experiments on multiple ship datasets in the synthetic aperture radar (SAR) and optical domains to verify the effectiveness of the method. The results show that the proposed method outperforms baseline by around 2.95% average precision (AP50), which achieves an effective USD system by complementing the information between domain-invariant representations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) target detection. Typical methods based on CNN have obtained favorable detection accuracy at the cost of high model complexity, and thus are difficult to be directly applied to real-time satellites on board as well as maritime rescue. To deal with this problem, this article proposes an efficient and lightweight target detection network incorporating soft quantization. First, to compensate for the lack of accuracy caused by lightweight networks, a feature fusion module called split bidirectional feature pyramid network is proposed to alleviate the interference of complex background on SAR images. Meanwhile, to adapt the lightweight network and the feature fusion module, a linear transformation module is presented to enhance the linear representation of the model via learnable parameters. Eventually, to make the model size smaller, a soft quantization algorithm is proposed to reduce the accuracy degradation caused by quantization errors. We validate the robustness of the model in several publicly available datasets. Experimental results show that our model achieves 97.0% detection accuracy on SAR ship detection dataset, with a 0.9% accuracy improvement compared to mainstream methods using less than $15\times $ the number of parameters and less than $6\times $ the number of flops. [ABSTRACT FROM AUTHOR]