KIEE - The Transactions of the Korean Institute of Electrical Engineers

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S. Y. Choi, April 2020, A Study on the Automation of Cam Heat Treatment Process using Deep Learning, Journal of The Korean Society of Industry Convergence, Vol. 23, No. 2, pp. 281-288

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S. H. Kwon, M. J. An, H. C. Lee, December 2018, Fault Detection and Classification of Process Cycle Signals using Density- based Clustering and Deep Learning, Journal of the Korean Institute of Industrial Engineers, Vol. 44, No. 6, pp. 475-482

3

R. Wang, K. Nie, T. Wang, Y. Yang, B. Long, 2020, Deep Learning for Anomaly Detection, Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 894-896

4

P. H. Cho, J. Y. Kim, B. H. Lee, S. D. Jeon, 2015, A Study of 154kV Transmition&Substation Operation Procedure, The 46th KIEE Summer Conference, pp. 443-444

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B. Kim, 2018, Design and Implementation of Image-base Fault Diagnosis System for Electric Power Transmission Equipment, Sogang Unversity Thesis Paper

6

J. M. Jeong, J. S. Kim, T. S. Yoon, J. B. Park, June 2018, Drone-based Power-line Tracking System, The transactions of The Koeran Institute of Electrical Engineers, Vol. 67, No. 6, pp. 773-781

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F. Zhang, W. Wang, Y. Zhao, P. Li, Q. Lin, L. Jiang, 2016, Automatic Diagnosis System of Transmission Line Abnormalities and Defects based on UAV, 2016 4th International Conference on Applied Robotics for Power Industry, pp. 1-5

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C. K. Lee, W. G. Baek, S. D. Kim, August 2012, Diagnosis and Evaluation of the Real Time Transformer by the Infrared Thermal Image Equipment, Journal of the Korean Society of Manufacturing Technology Engineers, Vol. 21, No. 4, pp. 666-671

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J. M. Jung, S. H. Park, Y. S. Lee, J. H. Gim, , The Development of Infrared Thermal Imaging Safety Diagnosis System Using Pearson’s Correlation Coefficient, Journal of the Koerean Solar Energy Society

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S. T. Oh, H. W. Kim, S. H. Cho, J. H. You, Y. S. Kwon, W. S. Ra, Y. K. Kim, November 2020, Development of a Compressed Deep Neural Network for Detecting Defected Electrical Substation Insulators using a Drone, Journal of Institute of Control, Robotics and Systems, Vol. 26, No. 11, pp. 884-890

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I. S. Sim, J. H. Lim, Y. W. Jang, J. H. You, S. T. Oh, Y. K. Kim, 2021, Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System, arXiv preprint arxiv:2108.00392

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R. Girshick, J. Donahue, T. Darrell, J. Malik, 2014, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587

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R. Girshick, 2015, Fast R-CNN, Proceedigns of the IEEE International Conference on Computer Vision, pp. 1440-1448

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S. Ren, K. He, R. Girshick, J. Sun, 2015, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems, Vol. 28, pp. 91-99

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J. Redmon, S. Divvala, R. Girshick, A. Farhadi, 2016, You Only Look Once: Unified, Real-Time Object Detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788

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J. Redmon, A. Farhadi, 2017, YOLO9000: Better, Faster, Stronger, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271

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J. Redmon, A. Farhadi, 2018, YOLOv3: An Incremental Improvement, arXiv preprint arXiv:1804.02767

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A. Bochkovskiy, C. Y. Wang, H. Y. M. Liao, 2020, YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint arXiv:2004.10934

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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, 2016, SSD: Single Shot MultiBox Detector, European Conference on Computer Vision, pp. 21-37

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T. Y. Lin, P. Goyal, R. Girshick, K. He, Dollar P, 2017, Focal Loss for Dense Object Detection, Proceedigns of the IEEE　International Conference on Computer Vision, pp. 2980-2988

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K. Simonyan, A. Zisserman, 2014, Very Deep Convolutional Networks for Large-Scale Image Recognitoin, arXiv preprint arXiv:1409.1556

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K. He, X. Zhang, S. Ren, J. Sun, 2016, Deep Residual Learning for Image Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778

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T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, 2017, Feature Pyramid Networks for Object Detection, Proceedings of IEEE　Conference on Computer Vision and Pattern Recognition, pp. 2117-2125

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K. He, X. Zhang, S. Ren, J. Sun, January 2015, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, pp. 1904-1915

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S. Lin, L. Qi, H. Qin, J. Shi, J. Jia, 2018, Path Aggregation Network for Instance Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759-8768

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M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi, 2016, XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks, European Conference on Computer Vision, pp. 525-542

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Z. Liu, B. Wu, W. Luo, X. Yang, W. Liu, K. T. Cheng, 2018, Bi-Real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm, Proceedings of the European Conference on Computer Vision, pp. 722-737

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F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer, 2016, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, arXiv preprint arXiv:1602.07360

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A. Krizhevsky, I. Sutskever, G. E. Hinton, 2012, Imagenet Classification with Deep Convolutional Neural Netowrks, Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105

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A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, H. Adam, 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861

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M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, 2018, MobileNetV2: Inverted Residuals and Linear Bottlenecks, Proceedings of the IEEE　Conference on Computer Vision and Pattern Recognition, pp. 4510-4520

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X. Zhang, X. Zhou, M. Lin, J. Sun, 2018, ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848-6856

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N. Ma, X. Zhang, H. T. Zheng, J. Sun, 2018, ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, Proceedings of the European Conference on Computer Vision, pp. 116-131

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C. Y. Wang, H. Y. M. Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, I. H. Yeh, 2020, CSPNet: A New Backbone That Can Enhance Learning Capability of CNN, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 390-391

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers