An Efficient Object Detection Network for Ammeter Inspection Task on Mobile Devices
Authors: 
Hong Yan, Jin Deng, Yiru Wan, Qiang Wang, Di Ma, Zhousheng ZhangAuthors Info & Claims
Hong Yan, Jin Deng, Yiru Wan, Qiang Wang, Di Ma, Zhousheng ZhangAuthors Info & ClaimsPages 104 - 108
Abstract
The inspection and evaluation of electric metering devices play an important role in the power grid, which is concerned with the safety of millions of households and consumes immense manpower. With the rapid development of the smart grid, there are urgent needs to evaluate the state of these devices in mobile scenarios by artificial intelligence. However, the limited memory resources and computation power of mobile devices present a huge challenge. In this study, we present an efficient object detection network called CSCNet for ammeter inspection on mobile devices, which achieves a good balance on detection precision and speed for the ammeter inspection task. Besides, the CSCNet improves small object detection precision to 90%. The experiment shows that the average detection time for an ammeter image is about 0.2 seconds with 92.87mAP on the chip of Qualcomm Snapdragon 855.
References
[1]
Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
[2]
Lowe D G. Object recognition from local scale-invariant features[C]//iccv. 1999, 99(2): 1150-1157.
[3]
Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]. 2005.
[4]
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
[5]
Girshick R, Donahue J, Darrell T, Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.
[6]
Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural processing letters, 1999, 9(3): 293-300.
[7]
Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.
[8]
Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99.
[9]
Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.
[10]
Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.
[11]
Howard A G, Zhu M, Chen B, Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.
[12]
Wang R J, Li X, Ling C X. Pelee: A real-time object detection system on mobile devices[C]//Advances in Neural Information Processing Systems. 2018: 1963-1972.
[13]
Huang G, Liu Z, Van Der Maaten L, Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.
[14]
Pinheiro P O, Lin T Y, Collobert R, Learning to refine object segments[C]//European Conference on Computer Vision. Springer, Cham, 2016: 75-91.
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Published In
August 2020
114 pages
ISBN:9781450388023
DOI:10.1145/3425577
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 23 January 2021
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ICCCV'20
ICCCV'20: 2020 the 3rd International Conference on Control and Computer Vision
August 23 - 25, 2020
Macau, China
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