[1] [1] 1徐芳， 刘晶红， 孙辉， 等. 光学遥感图像海面船舶目标检测技术进展［J］. 光学 精密工程， 2021， 29（4）： 916-931. doi: 10.37188/OPE.2020.0419XUF， LIUJ H， SUNH， et al. Research progress on vessel detection using optical remote sensing image［J］. Opt. Precision Eng.， 2021， 29（4）： 916-931. （in Chinese）. doi: 10.37188/OPE.2020.0419

[2] WANG Z Q, ZHOU Y, WANG F T et al. SDGH-net： ship detection in optical remote sensing images based on Gaussian heatmap regression[J]. Remote Sensing, 13, 499(2021).

[3] QIN H N, LI Y S, LEI J et al. A specially optimized one-stage network for object detection in remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 18, 401-405(2021).

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

[5] LI H, DENG L B, YANG C et al. Enhanced YOLO v3 tiny network for real-time ship detection from visual image[J]. IEEE Access, 9, 16692-16706(2021).

[6] XIE X Y, LI B, WEI X X. Ship detection in multispectral satellite images under complex environment[J]. Remote Sensing, 12, 792(2020).

[7] LIN T Y, MAIRE M, BELONGIE S et al. Microsoft COCO： Common Objects in Context[M]. Computer Vision-ECCV 2014, 740-755(2014).

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

[9] XIA G S, BAI X, DING J et al. DOTA： a large-scale dataset for object detection in aerial images[C], 3974-3983(2018).

[10] CHENG G, HAN J W, ZHOU P C et al. Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection[J]. IEEE Transactions on Image Processing, 28, 265-278(2019).

[11] LI K, WAN G, CHENG G et al. Object detection in optical remote sensing images： a survey and a new benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 296-307(2020).

[12] LIU Z K, YUAN L, WENG L B et al. A high resolution optical satellite image dataset for ship recognition and some new baselines[C], 24, 324-331(2017).

[13] CHEN K, WU M, LIU J et al. FGSD： A Dataset for Fine-grained Ship Detection in High Resolution Satellite Images[webpage]. arXiv, 2003-06832(2020). https：//arxiv.org/abs/2003.06832

[14] RAINEY K, STASTNY J. Object recognition in ocean imagery using feature selection and compressive sensing[C], 1-6(2012).

[15] ZHANG Z N, ZHANG L, WANG Y et al. ShipRSImageNet： a large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8458-8472(2021).

[16] ZOU Z X, SHI Z W. Random access memories： a new paradigm for target detection in high resolution aerial remote sensing images[J]. IEEE Transactions on Image Processing, 27, 1100-1111(2018).

[17] CHENG G, HAN J W, LU X Q. Remote sensing image scene classification： benchmark and state of the art[J]. Proceedings of the IEEE, 105, 1865-1883(2017).

[18] CHENG G, XIE X X, HAN J W et al. Remote sensing image scene classification meets deep learning： challenges， methods， benchmarks， and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3735-3756(2020).

[19] LAM D, KUZMA R, MCGEE K et al. xView： Objects in Context in Overhead Imagery[webpage]. arXiv, 1802-07856(2018). https：//arxiv.org/abs/1802.07856

[20] Airbus. Airbus Ship Detection Challenge， 2018[EB/OL]. Accessed： Dec, 2020(1). https：//www.kaggle.com/c/airbus-ship-detection/data

[21] GALLEGO A J, PERTUSA A, GIL P. Automatic ship classification from optical aerial images with convolutional neural networks[J]. Remote Sensing, 10, 511(2018).

[22] [22] 22姚力波， 张筱晗， 吕亚飞， 等. FGSC-23： 面向深度学习精细识别的高分辨率光学遥感图像舰船目标数据集［J］. 中国图象图形学报， 2021， 26（10）： 2337-2345.YAOL B， ZHANGX H， LYUY F， et al. FGSC-23： a large-scale dataset of high-resolution optical remote sensing image for deep learning-based fine-grained ship recognition［J］. Journal of Image and Graphics， 2021， 26（10）： 2337-2345. （in Chinese）

[23] LIU W, ANGUELOV D, ERHAN D et al. SSD： Single Shot MultiBox Detector[M]. Computer Vision-ECCV 2016, 21-37(2016).

[24] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(2017).

[25] KONG T, SUN F C, LIU H P et al. FoveaBox： beyound anchor-based object detection[J]. IEEE Transactions on Image Processing, 29, 7389-7398(2020).

[26] TIAN Z, SHEN C H, CHEN H et al. FCOS： fully convolutional one-stage object detection[C], 9626-9635.

[27] 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).

[28] CAI Z W, VASCONCELOS N. Cascade R-CNN： delving into high quality object detection[C], 6154-6162(2018).

[29] CHEN K, WANG J, PANG J et al. MMDetection： open MMLab detection toolbox and benchmark[webpage]. arXiv, 1906-07155(2019). https：//arxiv.org/abs/1906.07155