[1] KANJIR U, GREIDANUS H, OŠTIR K. Vessel detection and classification from spaceborne optical images: a literature survey[J]. Remote Sensing of Environment, 207, 1-26(2018).

[4] LIM H, CHAE D, YOO J H et al. Template matching-based target recognition algorithm development and verification using SAR images[J]. Journal of the Korea Institute of Military Science and Technology, 17, 364-377(2014).

[5] LIU Z K, WANG H Z, WENG L B et al. Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1074-1078(2016).

[6] LIU G, ZHANG Y S, ZHENG X W et al. A new method on inshore ship detection in high-resolution satellite images using shape and context information[J]. IEEE Geoscience and Remote Sensing Letters, 11, 617-621(2014).

[7] [7] REN S Q, HE K M, GIRSHICK R, et al. Faster RCNN: towards realtime object detection with region proposal wks[C]Proceedings of the 28th International Conference on Neural Infmation Processing Systems. Montreal: MIT Press, 2015: 91−99.

[8] [8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition. Las Vegas: IEEE, 2016: 779−788.

[9] [9] ZHANG H Y, WANG Y, DAYOUB F, et al. Varifocal: an IoUaware dense object detect[C]Proceedings of 2021 IEEECVF Conference on Computer Vision Pattern Recognition. Nashville: IEEE, 2021: 8514–8523.

[10] [10] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerl: Springer, 2014: 740−755.

[11] EVERINGHAM M, VAN GOOL L, WILLIAMS C K I et al. The Pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

[15] [15] SLER M, HOWARD A, ZHU M L, et al. MobileV2: inverted residuals linear bottlenecks[C]Proceedings of 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 456104520.

[17] [17] MA N N, ZHANG X Y, ZHENG H T, et al. Shuffle V2: practical guidelines f efficient CNN architecture design[C]Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 2018: 116−131.

[18] [18] ZHANG X Y, ZHOU X Y, LIN M X, et al. Shuffle: an extremely efficient convolutional neural wk f mobile devices[C]Proceedings of 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 6848−6856.

[19] [19] ZHANG Q L, YANG Y B. SA: shuffle attention f deep convolutional neural wks[C]Proceedings of 2021 IEEE International Conference on Acoustics, Speech Signal Processing. Tonto: IEEE, 2021: 2235−2239.