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智能机器人相关论文能引用的参考文献范例

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  智能机器人论文能引用的参考文献有哪些?一般的参考文献的引用,都是从知网中直接引用的,论文参考文献是研究的根本,也反映了作者的专业素养。参考文献的数量主要取决于你的文章长度,也会有相应的增加,以下是分享的部分关于智能机器人论文能引用的参考文献。


参考文献

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[2] Zou Z X, Shi Z W, Guo Y H, et al. Object detection in 20 years: a survey[J]. arXiv preprint arXiv:1905.05055, 2019.

[3] Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: a survey[J]. International Journal of Computer Vision, 2020, 128(2): 261-318.

[4] Qin T, Li P L, Shen S J. VINS-mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.

[5] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[6] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C] //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2001: 511-518.

[7] Viola P, Jones M J. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.

[8] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C] //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2005: 886-893.

[9] Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2008: 1-8.

[10] Felzenszwalb P F, Girshick R B, McAllester D. Cascade object detection with deformable part models[C] //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2010: 2241-2248.

[11] Everingham M, Eslami S M, Van Gool L, et al. The pascal visual object classes challenge: A retrospective[J]. International Journal of Computer Vision, 2015, 111(1): 98-136.

[12] Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.

[13] Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.

[14] Lin T Y, Maire M, Belongie S, et al. Microsoft COCO: common objects in context[C]. Proceedings of the European Conference on Computer Vision. Heidelberg: Springer, 2014: 740-755.

[15] Kuznetsova A, Rom H, Alldrin N, et al. The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale[J]. International Journal of Computer Vision, 2020, 128(7): 1956-1981.

[16] Lowe D G. Object recognition from local scale-invariant features[C]. Proceedings of the 7th IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 1999: 1150-1157.

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

[18] Campos C, Elvira R, Rodríguez J J G, et al. ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM[J]. IEEE Transactions on Robotics, 2021, 37(6): 1874-1890.

[19] Quan M X, Piao S H, Tan M L, et al. Tightly-coupled monocular visual-odometric SLAM using wheels and a MEMS gyroscope[J]. IEEE Access, 2019, 7: 97374-9738


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