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Multi-dimensional, multi-functional and multi-level attention in YOLO for underwater object detection

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Abstract

Underwater object detection is a prerequisite for underwater robots to achieve autonomous operation and ocean exploration. However, poor imaging quality, harsh underwater environments and concealed underwater targets greatly aggravate the difficulty of underwater object detection. In order to reduce underwater background interference and improve underwater object perception, we propose a multi-dimensional, multi-functional and multi-level attention module (mDFLAM). The multi-dimensional strategy first enhances the robustness of attention application by collecting valuable information in different target dimensions. The multi-functional strategy further improves the flexibility of attention calibration by capturing the importance of channel semantic information and the dependence of spatial location information. The multi-level strategy finally enriches the diversity of attention perception by extracting the intrinsic information under different receptive fields. In pre-processing and post-processing stages, cross-splitting and cross-linking stimulate the synergistic calibration advantage of multi-dimensional and multi-functional attention by redistributing channel dimensions and restoring feature states. In the attention calibration stage, adaptive fusion stimulates the synergistic calibration advantage of multi-level attention by assigning learnable parameters. In order to meet the high-precision and real-time requirements for underwater object detection, we integrate the plug-and-play mDFLAM into YOLO detectors. The full-port embedding further strengthens the semantic information expression by improving the feature fusion quality between scales. In underwater detection tasks, ablation and comparison experiments demonstrate the rationality and effectiveness of our attention design. In other detection tasks, our work shows good robustness and generalization.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge the financial supports from the National Natural Science Foundation of China under Grant 61370142, Grant 61802043, Grant 61272368, Grant 62176037 and Grant 62002041, in part by the Fundamental Research Funds for the Central Universities under Grant 3132016352 and Grant 3132021238, in part by the Dalian Science and Technology Innovation Fund under Grant 2018J12GX037, Grant 2019J11CY001 and Grant 2021JJ12GX028, in part by Liaoning Revitalization Talents Program under Grant XLYC1908007, in part by the Liaoning Doctoral Research Start-up Fund Project Grant 2021-BS-075, and in part by the China Postdoctoral Science Foundation under Grant 3620080307.

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Xin Shen, Xudong Sun, Huibing Wang & Xianping Fu

  2. Peng Cheng Laboratory, Shenzhen, 518000, China

    Xianping Fu

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  1. Xin Shen
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  2. Xudong Sun
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Correspondence to Xianping Fu.

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Shen, X., Sun, X., Wang, H. et al. Multi-dimensional, multi-functional and multi-level attention in YOLO for underwater object detection. Neural Comput & Applic 35, 19935–19960 (2023). https://doi.org/10.1007/s00521-023-08781-w

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