[1] WANG N, CHEN T K, LIU S M et al. Deep learning-based visual detection of marine organisms: a survey[J]. Neurocomputing, 532, 1-32(2023).

[2] [2] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[C]2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 779−788.

[3] [3] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 6517−6525.

[4] [4] REDMON J, FARHADI A. Yolov3: an incremental improvement[R]. Washington: University of Washington, 2018.

[5] [5] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed accuracy of object detection[EBOL]. (20200423)[20230526]. https:doi.g10.48550arXiv.2004.10934.

[6] [6] KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P, et al. A convolutional neural wk f modelling sentences[C]Proceedings of the 52nd Annual Meeting of the Association f Computational Linguistics (Volume 1: Long Papers). Baltime, Maryl: Association f Computational Linguistics, 2014.

[7] [7] GIRSHICK R. Fast RCNN[C]Proceedings of the 2015 IEEE International Conference on Computer Vision. Washington: IEEE, 2015: 1440−1448.

[8] REN S Q, HE K M, GIRSHICK R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[9] [9] HE K M, GKIOXARI G, DOLLÁR P, et al. Mask RCNN[C]2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017: 2980−2988.

[10] [10] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detect[M]LEIBE B, MATAS J, SEBE N, et al. 14th European Conference on Computer VisionECCV 2016. Amsterdam, The herls: Springer, 2016, 9905: 21−37.

[11] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[12] LIANG X, ZHANG J, ZHUO L et al. Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 1758-1770(2020).

[13] [13] FU K, LI J, MA L, et al. Intrinsic relationship reasoning f small object detection[EBOL]. (20200902)[20230526]. https:doi.g10.48550arXiv.2009.00833.

[14] YAN Z W, ZHENG H C, LI Y et al. Detection-oriented backbone trained from near scratch and local feature refinement for small object detection[J]. Neural Processing Letters, 53, 1921-1943(2021).

[15] WANG N, CHEN T K, KONG X J et al. Underwater attentional generative adversarial networks for image enhancement[J]. IEEE Transactions on Human-Machine Systems, 53, 490-500(2023).

[16] [16] LIU Z M, GAO G Y, SUN L, et al. HRD: Highresolution detection wk f small objects[C]2021 IEEE International Conference on Multimedia Expo (ICME). Shenzhen, China: IEEE, 2021: 1−6.

[17] [17] ZHU X K, LÜ S C, WANG X, et al. TPHYOLOv5: Improved YOLOv5 based on transfmer prediction head f object detection on dronecaptured scenarios[C]2021 IEEECVF International Conference on Computer Vision Wkshops (ICCVW). Montreal, Canada: IEEE, 2021: 2778−2788.

[19] [19] LIU Z, LIN Y T, CAO Y, et al. Swin transfmer: hierarchical vision transfmer using shifted windows[C]2021 IEEECVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada: IEEE, 2021.

[20] HE H J, LIN Y D, CHEN F et al. Inshore ship detection in remote sensing images via weighted pose voting[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3091-3107(2017).

[21] [21] CHANG J Y, OH H, LEE S J, et al. Ship detection f KOMPSAT3A optical images using binary features adaboost classification[C]IGARSS 20202020 IEEE International Geoscience Remote Sensing Symposium. Waikoloa, HI, USA: IEEE, 2020.

[22] CHEN X Q, WANG S Z, SHI C J et al. Robust ship tracking via multi-view learning and sparse representation[J]. The Journal of Navigation, 72, 176-192(2019).

[25] [25] LIU T, ZHOU B J, ZHAO Y S, et al. Ship detection algithm based on improved YOLO V5[C]2021 6th International Conference on Automation, Control Robotics Engineering (CACRE). Dalian, China: IEEE, 2021.

[26] HU J M, ZHI X Y, SHI T J et al. PAG-YOLO: a portable attention-guided YOLO network for small ship detection[J]. Remote Sensing, 13, 3059(2021).

[27] [27] YUAN L, CHEN Y P, WANG T, et al. Tokenstotoken ViT: Training vision transfmers from scratch on Image[C]2021 IEEECVF International Conference on Computer Vision. Montreal, QC, Canada: IEEE, 2021: 558−567.

[28] [28] REZATOFIGHI H, TSOI N, GWAK J, et al. Generalized intersection over union: a metric a loss f bounding box regression[C]2019 IEEECVF Conference on Computer Vision Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019.

[29] [29] ZHENG Z H, WANG P, LIU W, et al. DistanceIoU loss: faster better learning f bounding box regression[C]34th AAAI Conference on Artificial Intelligence. New Yk: IEEE, 2020.

[30] [30] DENG J, DONG W, SOCHER R, et al. Image: a largescale hierarchical image database[C]2009 IEEE Conference on Computer Vision Pattern Recognition. Miami, FL, USA: IEEE, 2009.

[31] [31] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[M]FLEET D, PAJDLA T, SCHIELE B, et al. 13rd European Conference on Computer VisionECCV 2014. Zurich, Switzerl: Springer, 2014, 8693: 740−755.

[32] 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, 20, 2593-2604(2018).