[1] WANG C B, ZHANG X Y, CONG L Z, et al.Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments[J].Evolving Systems, 2019, 10(4):649-658. [2] 黎经元, 厉小润, 赵辽英.基于边缘线分析与聚合通道特征的港口舰船检测[J].光学学报, 2019, 39(8):225-234. LI J Y, LI X R, ZHAO L Y.Port ship detection based on edge line analysis and aggregation channel characteristics[J].Acta Optica Sinica, 2019, 39(8):225-234.(in Chinese) [3] 李浩谊, 马春庭.基于改进的Scharr算法的海上舰船图像边缘检测[J].舰船电子工程, 2019, 39(3):103-106. LI H Y, MA C T.Image edge detection of marine ships based on improved Scharr algorithm[J].Ship Electronic Engineering, 2019, 39(3):103-106.(in Chinese) [4] ZHANG Y, LI Q Z, ZANG F G.Ship detection for visual maritime surveillance from non-stationary platforms[J].Ocean Engineering, 2017, 141(1):53-63. [5] 丁鹏, 张叶, 贾平, 等.基于视觉显著性的海面舰船检测技术[J].电子学报, 2018, 46(1):127-134. DING P, ZHANG Y, JIA P.Detection technology of sea surface ships based on visual saliency[J].Chinese Journal of Electronics, 2018, 46(1):127-134.(in Chinese) [6] SHI G M, SUO J D.Ship target detection based on visual attention[C]//Proceedings of International Conference on Signal Processing, Communications and Computing.Qingdao, China:[s.n.], 2018:1-4. [7] SHAO Z F, WANG L G, WANG Z Y, et al.Saliency-aware convolution neural Network for ship detection in surveillance video[J].IEEE Transactions on Circuits and Systems for Video Technology, 2019, 30(3):1-15. [8] ZHANG W, HE X J, LI W Y, et al.An integrated ship segmentation method based on discriminator and extractor[J].Image and Vision Computing, 2019, 93(1):1-12. [9] WANG Y C, NING X Y, LENG B H, et al.Ship detection based on deep Learning[C]//Proceedings of International Conference on Mechatronics and Automation.Tianjin, China:[s.n.], 2019:275-279. [10] 马啸, 邵利民, 金鑫, 等.改进的YOLO模型及其在舰船目标识别中的应用[J].电讯技术, 2019, 59(8):869-874. MA X, SHAO L M, JIN X, et al.Improved YOLO model and its application in ship target recognition[J].Telecommunications Technology, 2019, 59(8):869-874.(in Chinese) [11] 赵春晖, 周瑶.基于改进Faster R-CNN算法的舰船目标检测与识别[J].沈阳大学学报(自然科学版), 2018, 30(5):366-371, 380. ZHAO C H, ZHOU Y.Ship target detection and recognition based on improved Faster R-CNN algorithm[J].Journal of Shenyang University(Natural Science Edition), 2018, 30(5):366-371, 380.(in Chinese) [12] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once:unified, real-time object detection[EB/OL].[2020-07-10].https://arxiv.org/abs/1506.02640. [13] REDMON J, FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2020-07-10].https://arxiv.org/abs/1804.02767. [14] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778. [15] HUNG K Y, ZHANG Z K, JIANG J M.Real-time image super-resolution using recursive depthwise separable convolution network[J].IEEE Access, 2019, 7:99804-99816. [16] 彭秀平, 仝其胜, 林洪彬, 等.一种面向散乱点云语义分割的深度残差-特征金字塔网络框架[J].自动化学报, 2019, 39(1):1-10. PENG X P, TONG Q S, LIN H B, et al.A deep residual-feature pyramid network framework for semantic segmentation of scattered point clouds[J].Acta Automatica Sinica, 2019, 39(1):1-10.(in Chinese) [17] TANG J L, WANG D.Weed identification based on K-means feature learning combined with convolutional neural network[J].Computers and Electronics in Agriculture, 2017, 135(1):63-70. [18] EVERINGHAM M, GOOL L, WILLIAMS C K I, et al.The pascal visual object classes challenge[J].International Journal of Computer Vision, 2010, 88(2):303-338. [19] WANG Z, BOVIK A C, SHEIKH H R, et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing, 2004, 13(4):600-612. [20] 周志华.机器学习[M].北京:清华大学出版社, 2016. ZHOU Z H.Machine learning[M].Beijing:Tsinghua University Press, 2016.(in Chinese) [21] DOLLAR P, WOJEK C, SCHIELE B, et al.Pedestrian detection:a benchmark[C]//Proceedings of IEEE Conference on Computer Vision and Patten Recognition.Washington D.C., USA:IEEE Press, 2009:304-311. [22] REN S, HE K, 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. |