[1] GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 580- 587.

[2] 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.

[3] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.

[4] GirshickR. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1440- 1448.

[5] 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.

[6] LiuW, AnguelovD, ErhanD, et al. SSD: single shot MultiBox detector[M] ∥Leibe B, Matas J, Sebe N, et al. Lecture notes in computer science. Cham: Springer, 2016, 9905: 21- 37.

[7] Lin TY, DollarP, GirshickR, et al. Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 2017: 936- 944.

[8] RedmonJ, Farhadi A. YOLOv3: an incremental improvement[J/OL]. ( 2018-04-08)[2019-01-30]. https:∥arxiv.org/abs/1804. 02767.

[9] Lin TY, GoyalP, GirshickR, et al. Focal loss for dense object detection[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2999- 3007.

[10] Zhang SF, Wen LY, BianX, et al. Single-shot refinement neural network for object detection[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), June 18-23, 2018, Salt Lake City. New York: IEEE, 2018: 4203- 4212.

[11] Fu CY, LiuW, RangaA, et al. DSSD: deconvolutional single shot detector[J/OL]. ( 2017-01-23)[2019-01-30]. https:∥arxiv.org/abs/1701. 06659.

[12] Li ZX, Zhou F Q. FSSD: feature fusion single shot multibox detector[J/OL]. ( 2018-05-17)[2019-01-30]. https:∥arxiv.org/abs/1712. 00960.

[13] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. ( 2015-04-10)[2019-02-02]. https:∥arxiv.org/abs/1409. 1556.

[14] JeongJ, ParkH, Kwak N. Enhancement of SSD by concatenating feature maps for object detection[J/OL]. ( 2017-05-26)[2019-02-01]. https:∥arxiv.org/abs/1705. 09587.

[15] Zhao QJ, ShengT, Wang YT, et al. M2Det: a single-shot object detector based on multi-level feature pyramid network[J/OL]. ( 2019-01-06)[2019-02-01]. https:∥arxiv.org/abs/1705. 09587.

[16] 刘星, 陈坚, 杨东方, 等. 场景耦合的空对地多任务遥感影像智能检测算法[J]. 光学学报, 2018, 38(12): 1215008.

    Liu X, Chen J, Yang D F, et al. Scene-coupled intelligent multi-task detection algorithm for air-to-ground remote sensing image[J]. Acta Optica Sinica, 2018, 38(12): 1215008.

[17] GravesA. Supervised sequence labelling with recurrent neural networks: long short-term memory[M]. Berlin, Heidelberg: Springer, 2012: 37- 45.

[18] Cai ZW, Fan QF, Feris RS, et al. A unified multi-scale deep convolutional neural network for fast object detection[M] ∥Leibe B, Matas J, Sebe N, et al. Lecture notes in computer science. Cham: Springer, 2016, 9908: 354- 370.

[19] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[20] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

[21] Zeng X Y, Ouyang W L, Yan J J, et al. Crafting GBD-net for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(9): 2109-2123.

[22] DaiJ, LiY, HeK, et al. R-FCN: object detection via region-based fully convolutional networks[C]∥Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. USA: Curran Associates Inc., 2016: 379- 387.

[23] BellS, Zitnick CL, BalaK, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 2874- 2883.

[24] GidarisS, KomodakisN. Object detection via a multi-region and semantic segmentation-aware CNN model[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1134- 1142.

[25] Xu ML, Cui LS, LüP, et al. MDSSD: multi-scale deconvolutional single shot detector for small objects[J/OL]. ( 2018-08-19)[2019-02-02]. https:∥arxiv.org/abs/1805. 07009.

[26] Lin TY, MaireM, BelongieS, et al. Microsoft COCO: common objects in context[M] ∥Fleet D, Pajdla T, Schiele B, et al. Lecture notes in computer science. Cham: Springer, 2014, 8693: 740- 755.