딥 러닝 기반 열화상 도로 영상에서 실시간 블랙 아이스 영역 분할 연구 = Research on Real-time Black Ice Region Segmentation of Infrared Road Images Based on Deep Learning

이옥걸 2022년
논문상세정보
' 딥 러닝 기반 열화상 도로 영상에서 실시간 블랙 아이스 영역 분할 연구 = Research on Real-time Black Ice Region Segmentation of Infrared Road Images Based on Deep Learning' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 딥 러닝
  • 딥 컨볼루션
  • 블랙아이스
  • 에지 컴퓨팅
  • 영상 분할
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,008 0

0.0%

' 딥 러닝 기반 열화상 도로 영상에서 실시간 블랙 아이스 영역 분할 연구 = Research on Real-time Black Ice Region Segmentation of Infrared Road Images Based on Deep Learning' 의 참고문헌

  • [9] J. Shao and P. J. Lister. "An Automated Nowcasting Model of Road Surface Temperature and State for Winter Road Maintenance," Journal of Applied Meteorology and Climatology 35, 8 (1996): 1352-1361, accessed Jun 18, 2022.
    [2022]
  • [99] L. C. Chen, Y. Zhu, G. Papandreou, et al, Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation, Proceedings of the European Conference on Computer Vision(ECCV), 2018: 801-818.
    [2018]
  • [98] L. C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking Atrous Convolution for Semantic Image Segmentation, arXiv preprint arXiv:1706.05587, 2017.
    [2017]
  • [97] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Trans. on Pattern Analysis & Machine Intelligence, 2018,40(4):834−848.
    [2018]
  • [96] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. Semantic Image Segmentation with Deep Convolutional Nets and Fullyconnected CRFs, arXiv preprint arXiv:1412.7062, 2014.
    [2014]
  • [95] H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, "Pyramid Scene Parsing Network," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230-6239, Apr. 2017.
    [2017]
  • [94] O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI), pp. 234-241, May. 2015.
    [2015]
  • [93] K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-scale Image Recognition, arXiv preprint arXiv:1409.1556, 2014.
    [2014]
  • [92] Z. Chen, Research on Semantic Segmentation Based on Convolutional Neural Networks, A Master Thesis Submitted to Beijing Jiaotong University , June 2018. pp67-68
    [2018]
  • [91] https://zhuanlan.zhihu.com/p/72589970
  • [90] https://www.zhihu.com/question/41037974/answer/150522307, 2017.9.
  • [8] T. V. Samodurova,Estimation of Significance the Parameters, Influencing on Road Ice Formation (the Results of Computing Experiment), The 11th Inter-national Road Weather Conference. Sapporo, Japan: Standing International Road Weather Commission, 2002.
    [2002]
  • [89] M. D. ZEILER, R. Fergus, Stochastic Pooling for Regularization of Deep Convolutional Neural Networks, arXiv preprint arXiv:1301.3557, 2013.
    [2013]
  • [88] Y. L. Boureau, N. L. Roux, F. Bach, et al. Ask the Locals: Multi-way Local Pooling for Image Recognition, IEEE International Conference on Computer Vision. IEEE,2011:2651-2658.
  • [87] J. Gu, Z. Wang, J. Kuen, et al. Recent Advances in Convolutional Neural Networks, Pattern recognition, 2018, 77: 354-377.
    [2018]
  • [86] S. Ioffe, C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint arXiv:1502.03167, 2015.
    [2015]
  • [85] K. He, X. Zhang, S. Ren, et al, Delving Deep into Rectifiers: Surpassing Human-level Performance on ImageNet Classification, Proceedings of the IEEE international conference on computer vision. 2015: 1026-1034.
    [2015]
  • [84] AL Maas, AY Hannun and AY Ng, "Rectifier Nonlinearities Improve Neural Network Acoustic Models." Proc. icml. Vol. 30. No. 1. 2013.
    [2013]
  • [83] X. Glorot, A. Bordes, Y. Bengio, Deep Sparse Rectifier Neural Networks,Aistats. 2011, 15(106): 275.
    [2011]
  • [82] J. Rafferty, P. Shellito, NH. Hyman, et al. “Practice Parameters for Sigmoid Diverticulitis,” Diseases of the Colon & Rectum, 2006, 49(7): 939-944.
    [2006]
  • [81] Y. Lecun, L. Bottou, Y. Bengio, P. Haffiier, “Gradient-based Learning Applied to Document Recognition,” Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [1998]
  • [80] Y. LeCun, B. E. Boser, et al, Handwritten Digit Recognition with a Back-propagation Network, Advances in Neural Information Processing Systems, 1990, 2(2): 396-404.
    [1990]
  • [7] J. Norrman, Slipperiness on Roads-an Expert System Classification, Meteorological Applications, 7(1):27-36, 2000.
    [2000]
  • [79] D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning Internal Representations by Error Propagation,. California Univ San Diego La Jolla Inst for Cognitive Science,1985.
  • [78] K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics,1986,36(4): 193-202.
  • [77] D. H. Hubei, T. N. Wiesel, “Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex,” The Journal of physiology, 1962, 160(1): 106-154.
    [1962]
  • [76] M. L. Minsky, S. A. Papert, Perceptrons [M]. Cambridge: MIT press, 1969.
    [1969]
  • [75] F. Rosenblatt, Principles of Neuro Dynamics: Perceptrons and the Theory of Brain Mechanisms[M], Spartan Books, 1961.
    [1961]
  • [74] F. Rosenblatt, The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, 1958, 65(6): 386-408.
    [1958]
  • [73] W. S. McCulloch, W. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, Bltn Mathcal Biology 52, 99–115, 1990.
    [1990]
  • [72] J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 7132-7141.
    [2018]
  • [71] A. Mohamed, G. E. Dahl and G. Hinton, "Acoustic Modeling Using Deep Belief Networks," in IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 1, pp. 14-22, Jan. 2012.
    [2012]
  • [70] K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 770-778
    [2016]
  • [6] G. Y. Park, S. H. Lee, E. J. Kim, and B. Y. Yun, A Case Study on Meteorological Analysis of Freezing Rain and Black Ice Formation on the Load at Winter, Journal of Environmental Science International, vol. 26, no. 7, pp. 827-836, 2017.
    [2017]
  • [69] C. Szegedy et al., "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1-9.
    [2015]
  • [68] K. Simonyan, and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, CoRR, abs/1409.1556, 2015
    [2015]
  • [67] A, Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM,2017,60(6):84-90.
  • [66] R. Girshick, J. Donahue, T. Darrell, et al. Region Based Convolutional networks for Accurate Object Detection and Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(1):142-158.
  • [65] P. Dollar, R. Appel, S. Belongie, et al, Fast Feature Pyramids for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(8):1532-1545.
  • [64] I. J. Goodfellow, J. Pouget-abadie, M. Mirza, et al. Generative Adversarial Networks, arXiv:1406.2661, 2014.
    [2014]
  • [63] Y. Lecun, L. Bottou, Y. Bengio, et al. “Gradient-based Learning Applied to Document Recognition,” Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [1998]
  • [62] L. F. Wang, C. M. Yan, A Survey of Semantic Segmentation of Road Scenes, Advances in Lasers and Optoelectronics, 2021,58(12):120003.
  • [61] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, arXiv preprint arXiv:1412.7062, 2014.
    [2014]
  • [60] J. W. Liu, H. E. Li, X. L. Luo, Learning Technique of Probabilistic Graphical Models: A review, Acta Automatica Sinica, 2014,40(6): 1025−1044 (in Chinese with English abstract).
    [2014]
  • [5] https://news.mt.co.kr/mtview.php?no=2020021810285811085
  • [59] J. Long, E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation, IEEE Transactions on Pattern Analysis& Machine Intelligence(TPAMI). 2014, 39(4):640-651.
    [2014]
  • [58] H. Kuang, J. Wu, Survey of Image Semantic Segmentation Based on Deep Learning, Computer Engineering and Applications, 2019. 55(19):12-21.
    [2019]
  • [57] S. Deng, H. Zhao, J. Yin, et al, Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence, arXiv preprint arXiv:1909.00560, 2019.
    [2019]
  • [56] Y. Zhang, X. Ma, J. Zhang, et al. Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity, IEEE Network, 2019, 33(3): 58-64.
    [2019]
  • [55] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, et al, Generative Adversarial Networks, Advances in Neural Information Processing Systems, 2014, 3:2672-2680.
    [2014]
  • [54] V. Mnih, K. Kavukcuoglu, D. Silver, et al, “Human-level Control through Deep Reinforcement Learning,” Nature, 2015, 518(7540): 529-533.
    [2015]
  • [53] J. Devlin, M. W. Chang, et al, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805 (2018).
    [2018]
  • [52] Y. Mao, J. Zhang and K. B. Letaief, "Joint Task Offloading Scheduling and Transmit Power Allocation for Mobile-Edge Computing Systems," 2017 IEEE Wireless Communications and Networking Conference (WCNC), 2017, pp. 1-6.
    [2017]
  • [51] B. Li, B. C. Jia, W. Z. Cao, et al. Application prospect of edge computing in power demand response business, Grid Technology, 2018,42(1):79-87.
    [2018]
  • [50] B. Qi, Y. Xia, B. Li, et al, Home Energy Management System Based on Edge Computing: Architecture, Key Technologies and Implementation, Power Construction, 2018,39(3):33-41.
    [2018]
  • [4] Korea Traffic Accident Analysis System [Internet]. Available: http://taas.koroad.or.kr/.ᅠ
  • [49] Q. Zhang, W. Shi, et al, “Distributer Collaborative Execution on the Edges and its Application to AMBER Alerts,”IEEE Internet of Things Journal, 2018, 5(5):3580-3593.
    [2018]
  • [48] W. Shi, J. Cao, Q. Zhang, et al. Edge Computing: Vision and Challenges, Internet of Things Journal, IEEE, 2016,3(5):637-646.
    [2016]
  • [47] J. Cao, L. Y. Xu, R. Abdallah, et al. EdgeOS _ H: A Home Operating System for Internet of Everything,Proc of the 37th IEEE Int Conf on Distributed Computing Systems (ICDCS 2017). Piscataway, NJ:IEEE, 2017:1756-1764.
    [2017]
  • [46] X. Wu, R. Dunne, Q. Zhang, et al, Edge Computing Enabled Smart Firefighting: Opportunities and Challenges, Proc of the 5th ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies. New York: ACM, 2017:11:1-6.
    [2017]
  • [45] Y W Fu, X J Meng. Development Status and Countermeasures of Edge Computing Technology[J]. Tech China,2019(10):4-7.
  • [44] IEEE. ACM. The First IEEE/ACM Symposium on Edge Computing[EB/OL]. [2018-11- 05] .http://acm-ieee-sec.org/2016/
    [2018]
  • [43] M. Satyanarayanan, "The Emergence of Edge Computing," in Computer, vol. 50, no. 1, pp. 30-39, Jan. 2017.
    [2017]
  • [42] M. S. Elbamby, C. Perfecto, M. Bennis, et al, Edge Computing Meets Millimeter-wave Enabled VR:Paving the Way to Cutting the Cord, arXiv:1801.07614v3 [cs.IT] 9 Feb. 2018.
    [2018]
  • [41] W. S. Shi, H. Sun, J .Cao, et al. Edge Computing: A New Computing Model in the Era of Internet of Everything, Computer Research and Development, 2017, 54 (5):907-924.
    [2017]
  • [40] H. Wang, M. Zeng, Z. Xiong, et al. Finding Main Causes of Elevator Accidents Via Multi Dimensional Association Rule in Edge Computing Environment, 2017,14(011):39-47
    [2017]
  • [3] O. P. Ghim and T. F. Fwa, Mechanistic Interpretation of Braking Distance Specifications and Pavement Friction Requirements, Journal of the Transportation Research Board, Vol 2155, pp.145-157, Jul. 2010. DOI:10.3141/2155-16.
    [2010]
  • [39] I. Farris, L. Militano, M. Nitti, et al. MIFaaS: A Mobile-IoT-federation -as-a-service Model for Dynamic Cooperation of IoT Cloud Providers, Future Generation Computer Systems, 2017, 70:126-137.
    [2017]
  • [38] P. Liu, D. Willis, S. Banerjee, Paradrop: Enabling Lightweight Multi-tenancy at the Network Extreme Edge[C], Proc of IEEE/ACM Symp on Edge Computing. Piscataway, NJ:IEEE, 2016:50-57.
    [2016]
  • [37] P. G. Lopez, A. Montresor, D. Epema, et al. Edge-centric Computing Session and Challenges, ACM SIGCOMM Computer Communication Review, 2015,45(5):37-42.
    [2015]
  • [36] M. Satyanarayanan, P. Bahl, R. Caceres, et al. The Case for VM-based Cloudlets in Mobile Computing, IEEE Pervasive Computing, 2009, 8(4): 14-23.
    [2009]
  • [35] G. Pallis, A. Vakdi, Insight Perspectives for Content Deliver Networks, Communications of the ACM, 2006,49(1):101-106.
    [2006]
  • [34] J. Ravi, W. S. Shi, C. Z. Xu, Persongalized Email Management at Network Edges, IEEE Internet Computing, 2005,9(2):54-60.
    [2005]
  • [33] W. S. Shi, X. Z. Zhang, Y. F. Wang, et al. Edge Computing: Status and Prospects, Computer Research and Development, 2019, 56(1):69-89.
    [2019]
  • [31] Z. Zhou, X. Chen, E. Li, et al, Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing, Proceedings of the IEEE, 2019.
    [2019]
  • [30] https://www.idc.com/getdoc.jsp?containerId=prUS45213219
  • [2] G. Y. Park, S. H. Lee, E. J. Kim, and B. Y. Yun, "A Case Study on Meteorological Analysis of Freezing Rain and Black Ice Formation on the Load at Winter," Journal of Environmental Science International, Vol. 26, No. 7, pp. 827-836, Jun. 2017. DOI: 10.5322/JESI.2017.26.7.827
    [2017]
  • [29] H. Lee, K. Hwang, M. Kang, and J. Song, Black ice detection using CNN for the Prevention of Accidents in Automated Vehicle, International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas: USA, pp. 1189-1192, June. 2020.
    [2020]
  • [28] Y. E. Abdalla, M. T. Iqbal, and M. Shehata, Black Ice detection system using Kinect, IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, Apr. 2017.
    [2017]
  • [27] Q. Li, Y. W. Ji, and Z. P. Wang, Design of Road Icing Detection System Based on Opencv+Python, Journal of Shaanxi University of Science & Technology(Natural Science Edition), vol. 35, no. 2, pp. 158-164, 2017.
    [2017]
  • [26] P. Jonsson, Stationary Road Condition Monitoring Arrangement, EU [P] 13 192 172.8-1810, Jan. 7, 2014.
    [2014]
  • [25] P. Jonsson, J. Casselgren, B. Thornberg, “Road Surface Status Classification Using Spectral Analysis of NIR Camera Images,” IEEE Sensors Journal, 2015, 15(3): 1641-1656.
    [2015]
  • [24] K. McFall, T. Niittula, Results of Audio-visual Winter Road Condition Sensor Prototype[C], 11th Standing International Road Weather Congress, 2002: 1-9.
    [2002]
  • [23] R. Omer, L. Fu, An Automatic Image Recognition System for Winter Road Surface Condition Classification[C], International IEEE Conference on Intelligent Transportation Systems. IEEE, 2010:1375-1379.
    [2010]
  • [22] H. J. Lu, Pavement Ice Recognition Based on Pavement Temperature and Air Temperature, Instrumentation Technology and Sensors, 2010, 000(011):74-75.
    [2010]
  • [21] L. G. Gao, Detecting Black Ice on the Road with Infrared Sensors, Infrared, 2003(01):8.
    [2003]
  • [20] T. Liu, Q. Pan, J. Sanchez, et al. Prototype Decision Support System for Black Ice Detection and Road Closure Control, Intelligent Transportation Systems Magazine, IEEE, 2017,
    [2017]
  • [1] 2021 Edition (Statistics for 2020) Traffic Accident Statistical Analysis Report, Road Traffic Authority(KoROAD).
  • [19] X. Ma and C. Ruan, Method for Black Ice Detection on Roads Using Tri-wavelength Back Scattering Measurements, Appl. Opt, vol. 59, no. 24, pp. 7242-7246, 2020.
    [2020]
  • [18] P. Jonsson, Remote Sensor for Winter Road Surface Status Detection, SENSORS, Limerick: Ireland, pp. 1285-1288, Oct. 2011.
    [2011]
  • [17] T. A. Paulsen, P. Schmokel, Laser Road Surface Sensor-LRSS, Germany, Jul 01, 2004.
    [2004]
  • [16] Http://www. Vaisala.com.
  • [15] E. Ulf, Device, Method and System for Determining the Road Surface Condition[P], United States: US 7.224.453 B2, May.2007.
    [2007]
  • [14] M. Electronics, Black Ice Detection and Warning System: USA20080129541A1 [P], 2008-06-05.
    [2008]
  • [13] C. Zhou, Design and Experimental Study of Airborne Optical Fiber Icing Detector[D], Huazhong University of Science and Technology, 2014.
    [2014]
  • [136] https://developer.nvidia.com/jetpack-sdk-50dp
  • [135] https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/ jetson-nano/
  • [134] https://www.leiphone.com/category/industrynews/2L09czKJlJw8IaAo.html
  • [133] https://baike.baidu.com/item/NPU/17905535?fr=aladdin
  • [132] D. H. Yoon, et al. In-Datacenter PerformanceAnalysis of a Tensor Processing Unit [J]. 2017: 1-12.
    [2017]
  • [131] S. Potluri, A. Fasih, L. K. Vutukuru, et al. CNN-Based High Performance Computing for Real Time Image Processing on GPU, The Workshop on Nonlinear Dynamics & Synchronization & Intl Symposium on Theoretical-Electrical Engineering. IEEE, 2011: 1-7.
    [2011]
  • [130] F. NASSE, C. THURAU, G. A. FINK, Face Detection Using GPU-Based Convolutional Neural Networks, Lecture Notes in Computer Science, 2009, 5702: 83-90.
    [2009]
  • [12] S. Y. Gao, Research on Ice Detection Method of Flat Film Sensor[D], Wuhan, Huazhong University of Science and Technology Library, 2008.
    [2008]
  • [129] J. Long,E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015:3431-3440.
  • [128] D. P. Kingma and J. Ba. "Adam: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980, 2014.
    [2014]
  • [127] Data Quality Management Guidelines for Artificial Intelligence Learning V1.0, Ministry of Science and ICT, NIA, Korea Information and Communication Technology Association, 2021.02.
    [2021]
  • [126] P. Q. Wang, et al. "Understanding Convolution for Semantic Segmentation," 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 2018.
    [2018]
  • [125] G. Papandreou, I. Kokkinos, P. A. Savalle. Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection, Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015, 390-399
    [2015]
  • [124] M. Holschneider, R. Kronland-Martinet, J. Morlet, et al. “A real-time algorithm for signal analysis with the help of the wavelet transform,” Wavelets, 1990, 286-297
    [1990]
  • [123] D. Hien, A Guide to Receptive Field Arithmetic for Convolutional Neural Networks,Medium. com, 2017.
    [2017]
  • [122] W. Luo, Y. Li, R. Urtasun, et al, Understanding the Effective Receptive Field in Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 2016:4898-4906.
    [2016]
  • [121] G. B. Huang, H. Zhou, X. Ding, and R. Zhang, Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 42, pp. 513–529, 2012 2091 -2106.
    [2012]
  • [120] G. B. Huang, Q. Y. Zhu, and C. K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, in In Proc. International Joint Conference on Neural Networks(IJCNN’2004), (Budapest, Hungary), July 25-29, 2004.
    [2004]
  • [119] C. Eliasmith and C. H. Anderson, Neural Engineering: Computation, Representation, and Dynamics in Neuro Biological Systems, MIT Press,Cambridge, MA, 2003.
    [2003]
  • [118] M. D. Zeiler, D. Krishnan, G. W. Taylor, et al, Deconvolutional Networks, Computer Vision and Pattern Recognition. IEEE, 2010:2528-2535.
    [2010]
  • [117] J. Zhang, Semantic Image Segmentation Method Based on Deep Learning,A Master Thesis Submitted to University of Electronic Science and Technology of China, June 2018, pp45
    [2018]
  • [116] Y. Fisher, K. Vladlen, Multi-scale Context Aggregation by Dilated Convolutions, arXiv preprint arXiv:1511.07122,2015.
  • [115] X. Su, G. Sperli, V. Moscato, A. Picariello, C. Esposito, C. Choi, An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications, IEEE Transactions on Industrial Informatics, 2019, 15(7): 4266− 4275
    [2019]
  • [114] H. Lin, Z. H. Chen, L. S. Wang, Offloading for Edge Computing in Low Power Wide Area Networks with Energy Harvesting, IEEE Access, 2019, 7: 78919−78929
    [2019]
  • [113] X. H. Deng, P. Y. Guan, Z. W. Wan, E. L. Liu, J. Luo, et al, “Integrated Trust Based Resource Cooperation in Edge Computing,” Journal of Computer Research and Development, 2018, 55(3): 449−477
    [2018]
  • [112] L. Z. Li, K. Ota, M. X. Dong, Deep Learning for Smart Industry: Efficient Manufacture Inspection System with Fog Computing, IEEE Transactions on Industrial Informatics, 2018, 14(10): 4665−4673
    [2018]
  • [111] J. Ren, Y. D. Guo, D. Y. Zhang, Q. Q. Liu, Y. X. Zhang, Distributed and Efficient Object Detection in Edge Computing: Challenges and Solutions, IEEE Network, 2018, 32(6): 137-143
    [2018]
  • [110] J. J. Ren, H. C. Wang, T. T. Hou, S. Zheng, C. S. Tang, Federated Learning-based Computation Offloading Optimization in Edge Computing-supported Internet of things, IEEE Access , 2019, 7:69194-69201
    [2019]
  • [10] A. Troiano, E. Pasero and L. Mesin, "New System for Detecting Road Ice Formation," in IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 3, pp. 1091-1101, March 2011.
    [2011]
  • [109] A. Canziani, A. Paszke, E. Culurciello, An Analysis of Deep Neural Network Models for Practical Applications, arXiv preprint arXiv: 1605.07678, 2016.
    [2016]
  • [108] P. Qiao, Research on Traffic Flow Detection Based on Deep Learning and Edge Task Offloading, XiDian Univertity in partial fulfillment of the requirements for the degree of Master, May, 2019, pp:19-20
    [2019]
  • [107] W. S. Shi, H. Sun, J. Cao, et al. “Edge Computing-an Emerging Computing Model for the Internet of Everything Era,” Journal of computer research and development, 2017, 54(5): 907-924.
    [2017]
  • [106] V. Turner, J. F. Gantz, D. Reinsel, et al. The Digital Universe of Opportunities: Rich data and the Increasing Value of the Internet of Things, IDC Analyze the Future, 2014, 5.
    [2014]
  • [105] A. Chaurasia and E. Culurciello, "LinkNet: Exploiting encoder representations for efficient semantic segmentation," IEEE Visual Communications and Image Processing, pp. 1-4, Dec. 2017.
    [2017]
  • [104] J. Tompson, R. Goroshin, A. Jain, et al. Efficient Object Localization Using Convolutional Networks, IEEE Conference Computer Vision and Pattern Recognition.2015:648-656.
  • [103] K. He, X. Zhang, S. Ren S, et al. Delving Deep into Rectifiers: Surpassing Human-level Performance on Imagenet Classification, Proceedings of the IEEE International Conference on Computer Vision.2015:1026-1034.
  • [102] V. Badrinarayanan, A. Kendall, R. Cipolla, Segnet: A Deep Convolutional Encoder-decoder Architecture for Image Segmentation, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2015,39(12):2481−2495. [doi: 10.1109/TPAMI.2016.2644615]
    [2015]
  • [101] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, Jun. 2016. arXiv:1606.02147
    [2016]
  • [100] F. Chollet, Xception: deep learning with depthwise separable convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitio n,2017: 1251-1258.
  • Piezoelectric Device Based Ice Sensor
    Y. Lin 9): 6-7 [2001]
  • Edge Computing: Vision and Challenges
    W. Shi Fellow 3(5):637-646 [2016]