Refined marine object detector with attention-based spatial pyramid pooling networks and bidirectional feature fusion strategy

Xu, Fengqiang; Wang, Huibing; Sun, Xudong; Fu, Xianping

doi:10.1007/s00521-022-07264-8

Cited by 30 publications

(7 citation statements)

References 29 publications

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“…Therefore, this paper sets up three groups of submarine maneuvering state inversion simulation experiments. The effectiveness of the proposed method is verified by comparing the results of submarine maneuverability state inversion with MFENet [ 44 ], SA-SPPN [ 45 ], DAFNet [ 46 ], and APAN [ 47 ] algorithms. The evaluation criteria of the algorithm are mean average accuracy (mAP) and overall accuracy (Acc).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

Kong

Yang

Cai

et al. 2023

Computational Intelligence and Neuroscience

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As the noise reduction performance of submarines continues to improve, it is difficult to detect and track submarines through acoustic detection techniques. Therefore, nonacoustic submarine detection techniques are becoming more and more important. The submarine movement will leave a wake vortex, and the information of the wake vortex can be used to invert the maneuvering state of the submarine. However, the wake vortex is constantly dissipated in the evolution process, and the strength of the wake vortex is constantly reduced, resulting in the gradual weakening of the characteristics of the wake vortex, which makes the inversion of submarine operating state difficult and less accurate. In order to solve the above problems, this paper proposes an improved wake vortex-based inversion method for submarine maneuvering state. Firstly, a random finite set of submarine wake vortex observation features is established to obtain the feature with the highest correlation degree with submarine maneuvering state in the random finite set. Secondly, the multiscale fusion module and attention mechanism are used to re-encode the weak features of the wake vortex image, and the salient features of the wake vortex image are extracted. Finally, the manipulation state of the wake vortex image is retrieved by the extracted salient features. The experimental results show that the average inversion accuracy of the proposed algorithm is improved by 1.27% in terms of manipulating state inversion of weak feature wake vortex images. The algorithm in this paper can realize the inversion of submarine maneuvering state in the case of weak submarine wake vortex image features and incomplete feature information. It provides the basis for the detection technology based on the submarine wake characteristics.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

Kong

Yang

Cai

et al. 2023

Computational Intelligence and Neuroscience

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“…It bridges the gap between the two modalities driven by the attention mechanism to sharpen salient object contours guided by edge information. Xu F et al [ 19 , 20 ] considered that due to problems such as color cast and blur in underwater images, the features extracted directly from the backbone network often lack interesting and distinguishable features, which affects the performance of marine target detection. A novel exemplary ocean object detector based on an attention-based spatial pyramid pooling network and bidirectional feature fusion strategy is proposed to alleviate feature weakening and solve the ocean object detection problem.…”

Section: Related Workmentioning

confidence: 99%

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Meng,

Si,

Mao

et al. 2024

PLoS ONE

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Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects’ spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.

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“…In Ref. 35, an object detector with an attention-based spatial pyramid pooling network (SA-SPPN) and a bi-directional feature fusion strategy is proposed to solve marine object detection problems.…”

Section: Related Workmentioning

confidence: 99%

MFDNN: multi-scale feature-weighted dual-neck network for underwater object detection

Xu,

Zhang,

Tong

2024

J. Electron. Imag.

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With the rapid development of object detection technology, underwater object detection has gradually become a hot topic. Due to the complex underwater environment, some object detection algorithms still encounter difficulties in detecting targets similar to the background. For the above problems, we propose a multi-scale featureweighted dual-neck network (MFDNN) for underwater object detection. Our contribution is mainly divided into three parts. First, an enhanced feature extraction network, namely the dual-neck network, is designed to process and reuse the features extracted from the backbone network. Second, an attention mechanism is embedded in one of the neck networks to reweight features and pay more attention to important features. In addition, we introduce the adaptively spatial feature fusion mechanism to adaptively weight the features extracted at multiple scales. As demonstrated in comprehensive experiments, the mean average precision of our MFDNN can reach 87.79% and 86.51% on the underwater datasets URPC2019 and URPC2020, respectively.

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Refined marine object detector with attention-based spatial pyramid pooling networks and bidirectional feature fusion strategy

Cited by 30 publications

References 29 publications

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

MFDNN: multi-scale feature-weighted dual-neck network for underwater object detection

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