[1] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. [C]∥IEEE Conference on Computer Vision and Pattern Recognition(2014).

[2] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Proceedings of the 25 ^th International Conference on Neural , 1, 1097-1105(2012).

[3] 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, 770-778(2016).

[4] Zhang X N, Zhong X, Zhu R F et al. Scene classification of remote sensing images based on integrated convolutional neural networks[J]. Acta Optica Sinica, 38, 1128001(2018).

[5] Ye G L, Sun S Y, Gao K J et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 54, 081003(2017).

[6] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

[7] Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 55, 081005(2018).

[8] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://ieeexplore.ieee.org/document/7478072/

[9] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017). http://www.ncbi.nlm.nih.gov/pubmed/28060704

[10] Gamal M, Siam M, Abdel-Razek M. ShuffleSeg: real-time semantic segmentation network. [C]∥IEEE Conference on Computer Vision and Pattern Recognition(2018).

[11] Song Q S, Zhang C, Chen Y et al. Road segmentation using full convolutional neural networks with conditional random fields[J]. Journal of Tsinghua University(Science and Technology), 58, 725-731(2018).

[12] Iandola F N, Han S, Moskewicz M W et al. SqueezeNet: Alexnet-level accuracy with 50× fewer parameters and <0.5 MB model size. [C]∥IEEE Conference on Computer Vision and Pattern Recognition(2016).

[13] Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888-905(2000). http://doi.ieeecomputersociety.org/10.1109/34.868688

[14] Zhu W, Qu J Y, Wu R B. Straight convolutional neural networks algorithm based on batch normalization for image classification[J]. Journal of Computer-Aided Design & Computer Graphics, 29, 1650-1657(2017).

[15] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥Proceedings of the 32nd International Conference on Machine Learning, 37, 448-456(2015).

[16] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. [C]∥IEEE Conference on Computer Vision and Pattern Recognition(2015).

[17] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. [C]∥IEEE Conference on Medical Image Computing and Computer-Assited Intervention, 234-241(2015).

[18] [18] AbadiM, AgarwalA, BarhamP, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems[J]. arXiv preprint arXiv:1603.04467,2016.

[19] [19] ChetlurS, WoolleyC, VandermerschP, et al. cuDNN: efficient primitives for deep learning[J]. arXiv:1410.0759, 2014.

[20] Zhou B L, Zhao H, Puig X et al. Semantic understanding of scenes through the ADE20K dataset[J]. International Journal of Computer Vision, 127, 302-321(2019). http://link.springer.com/article/10.1007/s11263-018-1140-0

[21] Garcia-Garcia A, Orts-Escolano S, Oprea S et al. A survey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing, 70, 41-65(2018). http://www.sciencedirect.com/science/article/pii/S1568494618302813