[1] [1] Chen Y M. Current situation and future trend of Chinese ports[J]. China Water Transp, 2019(6): 7.

[2] [2] Xing X J. Some thoughts on bulk cargo handling industry of domestic port[J]. Hoist Con Mach, 2019(10): 1.

[3] [3] Ji B S. A study on the design of grab bucket control program[J]. J Nantong Vocational Tech Shipping Coll, 2012, 11(4): 76–79.

[4] [4] Yao Z Y. Research on the application of object detection technology based on deep learning algorithm[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.

[5] [5] Manana M, Tu C L, Owolawi P A. A survey on vehicle detection based on convolution neural networks[C]//Proceedings of the 3rd IEEE International Conference on Computer and Communications, 2017: 1751–1755.

[6] [6] Dai W C, Jin L X, Li G N, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electron Eng, 2018, 45(12): 180350.

[7] [7] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580–587.

[8] [8] Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440–1448.

[9] [9] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

[10] [10] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779–788.

[11] [11] Redmon J, Farhadi A. YOLOv3: an incremental improvement[Z]. arXiv:1804.02767, 2018.

[12] [12] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9): 1904–1916.

[13] [13] Howard A G, Zhu M L, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[Z]. arXiv:1704.04861, 2017.

[14] [14] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936–944.

[15] [15] 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, 2016: 770–778.

[16] [16] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[Z]. arXiv:1502.03167, 2015.

[17] [17] LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Comput, 1989, 1(4): 541–551.

[18] [18] Maas A L, Hannum A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models[C]//Proceedings of the 30th International Conference on Machine Learning, 2013.

[19] [19] Sandler M, Howard A, Zhu M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4510–4520.

[20] [20] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[ Z]. arXiv:1511.07122, 2015.