[3] KRIZHEVSKY ASUTSKEVER IHINTON G E.Image-Net classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.Lake TahoeNV: NIPS2012:1097-1105.
[4] SIMONYAN KZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations.Banff: ICLR2014: 1-14.
[5] SZEGEDY CVANHOUCKE VIOFFE Set al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las VegasNV:IEEE2016:2818-2826.
[6] HE K MZHANG X YREN S Qet al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las VegasNV:IEEE2016:770-778.
[7] HUANG GLIU ZVAN DER MAATEN Let al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.HonoluluHI:IEEE2017:4700-4708.
[8] GIRSHICK RDONAHUE JDARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Colum busOH:IEEE2014:580-587.
[9] REDMON JFARHADI A.YOLO9000:betterfasterstronger[C]//IEEE Conference on Computer Vision and Pattern Recognition.HonoluluHI:IEEE2017:6517-6525.
[10] REDMON JDIVVALA SGIRSHICK R,et al.You only look once:unifiedreal-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las VegasNV:IEEE2016:779-788.
[11] WANG Y YWANG CZHANG H.Combining a single shot multibox detector with transfer learning for ship detection using Sentinel-1 SAR images[J].Remote Sensing Letters20189(8):780-788.