• Electronics Optics & Control
  • Vol. 32, Issue 4, 52 (2025)
LI Dongqin1, PENG Qi2, and WU Yang2
Author Affiliations
  • 1School of Naval Architecture and Intelligent Manufacturing, Jiangsu Maritime Institute, Nanjing 211000, China
  • 2School of Marine and Offshore Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China
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    DOI: 10.3969/j.issn.1671-637x.2025.04.008 Cite this Article
    LI Dongqin, PENG Qi, WU Yang. Ship Object Detection with Lightweight Attention Mechanism and Cross-Scale Fusion[J]. Electronics Optics & Control, 2025, 32(4): 52 Copy Citation Text show less
    References

    [2] CHAI B Q, CHEN L, SHI H, et al. Marine ship detection method for SAR image based on improved Faster RCNN[C]//SAR in Big Data Era (BIGSARDATA). Nanjing: IEEE, 2021: 1-4.

    [3] JIN L, LIU G D. An approach on image processing of deep learning based on improved SSD[J]. Symmetry, 2021, 13(3): 495.

    [4] GAO Y L, WU Z Y, REN M, et al. Improved YOLOv4 based on attention mechanism for ship detection in SAR images[J]. IEEE Access, 2022, 10: 23785-23797.

    [5] ZOU G J, ZHANG Z A. Pedestrian target detection algorithm based on improved YOLOv5[C]//International Conference on Internet of Things and Machine Learning (IoTML 2023). Singapore: SPIE, 2023: 1293719.

    [6] YU C S, SHIN Y. SAR ship detection based on improved YOLOv5 and BiFPN [J]. ICT Express, 2024, 10(1): 28-33.

    [8] XUE Y, YUAN Z M. HDAM: heuristic difference attention module for convolutional neural networks[J]. Journal on Internet of Things, 2022, 4(1): 57-67.

    [9] TANG Y H, HAN K, GUO J Y, et al. GhostNetv2: enhance cheap operation with long-range attention[J]. Advances in Neural Information Processing Systems, 2022, 35: 9969-9982.

    [10] HAN K, WANG Y H, TIAN Q, et al. GhostNet: more features from cheap operations[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 1577-1586.

    [11] LIN T-Y, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 936-944.

    [12] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City: IEEE, 2018: 8759-8768.

    [13] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Se-attle: IEEE, 2020: 10781-10790.

    [14] HE J B, ERFANI S, MA X J, et al. Alpha-IoU: a family of power intersection over union losses for bounding box regression[R]. Los Alamos: arXiv Preprint, 2022: arXiv: 2110. 13675.

    [15] SHAO Z F, WU W J, WANG Z Y, et al. SeaShips: a large-scale precisely annotated dataset for ship detection[J]. IEEE Transactions on Multimedia, 2018, 20(10): 2593-2604.

    [17] ZHOU W N, PENG Y J. Ship detection based on multi-scale weighted fusion[J]. Displays, 2023, 78: 102448.