[3] [3] CHENG G, HAN J W, GUO L, et al. Object detection in remote sensing imagery using a discriminatively trained mixture model [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85: 32-43.

[4] [4] LIU G, SUN X, FU K, et al. Aircraft recognition in high-resolution satellite images using coarse-to-fine shape prior [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 573-577.

[5] [5] ARANDJELOVI O. Object matching using boundary descriptors [C]//Proceedings of the British Machine Vision Conference. Surrey, UK: BMVA Press, 2012.

[6] [6] KAHAKI S M M, NORDIN M J, ASHTARI A H, et al. Deformation invariant image matching based on dissimilarity of spatial features [J]. Neurocomputing, 2016, 175: 1009-1018.

[7] [7] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.

[8] [8] GIRSHICK R. Fast R-CNN [C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.

[9] [9] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2015: 91-99.

[10] [10] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.

[11] [11] REDMON J, FARHADI A. YOLO9000: better, faster, stronger [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 7263-7271.

[12] [12] REDMON J, FARHADI A. YOLOv3: an incremental improvement [J]. arXiv: 1804.02767, 2018.

[13] [13] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector [C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 21-37.

[15] [15] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.

[16] [16] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. [2019-06-01]. https: //arxiv.org/pdf/1409.1556.pdf.

[17] [17] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module [C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 3-19.

[18] [18] 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, 2017, 39(6): 1137-1149.

[19] [19] BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS—improving object detection with one line of code [C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017.

[20] [20] EVERINGHAM M, ESLAMI S M A, VAN GOOL L, et al. The PASCAL visual object classes challenge: a retrospective [J]. International Journal of Computer Vision, 2015, 111(1): 98-136.

[21] [21] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift [DB/OL]. (2015-05-02)[2019-12-01]. https: //arxiv.org/abs/1502.03167.