Threat detection in transmission scenario based on TATLNet

LI Mei; GUO Fei; ZHANG Li-zhong; WANG Bo; ZHANG Jun-ling; LI Zhao-tong

doi:10.13374/j.issn2095-9389.2019.09.15.004

Volume 42 Issue 4

Apr. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(4): 509-515

LI Mei, GUO Fei, ZHANG Li-zhong, WANG Bo, ZHANG Jun-ling, LI Zhao-tong. Threat detection in transmission scenario based on TATLNet[J]. Chinese Journal of Engineering, 2020, 42(4): 509-515. doi: 10.13374/j.issn2095-9389.2019.09.15.004

Citation:

LI Mei, GUO Fei, ZHANG Li-zhong, WANG Bo, ZHANG Jun-ling, LI Zhao-tong. Threat detection in transmission scenario based on TATLNet[J]. Chinese Journal of Engineering, 2020, 42(4): 509-515. doi: 10.13374/j.issn2095-9389.2019.09.15.004

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Threat detection in transmission scenario based on TATLNet

doi: 10.13374/j.issn2095-9389.2019.09.15.004

1.
Ningxia Electric Power Co. Ltd., Yinchuan 750001, China
2.
Wuzhong Power Supply Company of Ningxia Electric Power Co. Ltd., Wuzhong 751101, China
3.
Shandong Luneng Software Technology Co. Ltd., Jinan 250001, China
4.
College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China

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Corresponding author: E-mail: s18070027@s.upc.edu.cn
Received Date: 2019-09-15
Publish Date: 2020-04-01

Abstract

Abstract

The operation of cranes and other large machinery threatens the safety of transmission lines. In order to solve this problem in the transmission scenario, the research from the aspects of data enhancement, network structure and the hyperparameters of the algorithm were performed. And a new end-to-end transmission line threat detection method based on TATLNet were proposed in this paper, which included the suspicious areas generation network VRGNet and threat discrimination network VTCNet. VRGNet and VTCNet share part of the convolution network for feature sharing and we used the model compression to compress the model volume and improved the detection efficiency. The method can realize accurate detection of large-scale machinery invading in the transmission scene from the perspective of computer vision and system engineering. To mend the insufficient training data, the data set was expanded by a combination of various data enhancement techniques. The sufficient experiments were carried out to explore the multiple hyperparameters of this method, and its optimal configuration was studied by synthesizing detection accuracy and inference speed. The research results are sufficient. With increase in the number of grids, the accuracy and recall first increase and then decrease, whereas, the detection efficiency decreases rapidly with increase in the number of grids. Considering the detection accuracy and reasoning speed, 9 × 9 is the optimal division strategy. With the increase in the input image resolution, the detection accuracy increases steadily and detection efficiency decreases gradually. To balance the detection accuracy and inference efficiency, 480 × 480 is selected as the final image input resolution. Experimental results and field deployment demonstrate that compared with other lightweight object detection algorithms, this method has better accuracy and efficiency in large-scale machinery invasion detection such as cranes in transmission fields, and meets the demands of practical applications.
- deep learning,
- threat detection,
- feature sharing,
- transmission scene,
- lightweight network

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References(25)

References

[1]	Minker G A. Transmission Line Safety Monitoring System: U.S. Patent, 6377184. 2002-4-23
[2]	羅霞, 張良勇, 羅文金, 等. 基于熱釋電紅外傳感器的無人機巡檢控制系統研究. 科技經濟導刊, 2019, 27(8):3 Luo X, Zhang L Y, Luo W J, et al. Research on UAV patrol control system based on pyroelectric infrared sensor. Technol Econom Guide, 2019, 27(8): 3
[3]	Lu Y X, Kumar A, Zhai S F, et al. Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, 2017: 5334
[4]	He Y H, Zhang X Y, Sun J. Channel pruning for accelerating very deep neural networks // Proceedings of the IEEE International Conference on Computer Vision. Venice, 2017: 1389
[5]	Han S, Mao H Z, Dally W J. Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding[J/OL]. arXiv preprint (2016-02-15)[2019-09-15]. https://arxiv.org/abs/1510.00149
[6]	Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets // Advances in Neural Information Processing Systems. Montreal, 2014: 2672
[7]	段樹忠, 魏可強. 輸電線路主動預警式防外力破壞監控系統研究. 城市建設理論研究, 2017(15):6 Duan S Z, Wei K Q. Research on active early warning monitoring system for preventing external force damage of transmission lines. Theor Res Urban Constr, 2017(15): 6
[8]	郭圣, 曾懿輝, 張紀賓, 等. 輸電線路防外力破壞智能監控系統的應用. 廣東電力, 2018, 31(4):139 Guo S, Zeng Y H, Zhang J B, et al. Application of intelligent monitoring system for external force damage prevention for transmission lines. Guangdong Electr Power, 2018, 31(4): 139
[9]	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436 doi: 10.1038/nature14539
[10]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks // Advances in Neural Information Processing Systems. Lake Tahoe, 2012: 1097
[11]	Papageorgiou C P, Oren M, Poggio T. A general framework for object detection // Sixth International Conference on Computer Vision. Tampa, 1998: 555
[12]	Jiao L, Zhang F, Liu F, et al. A survey of deep learning-based object detection. IEEE Access, 2019(7): 128837
[13]	Zou Z, Shi Z, Guo Y, et al Object detection in 20 years: a survey[J/OL]. arXiv preprint (2019-05-13)[2019-09-15]. https://arxiv.org/abs/1905.05241
[14]	Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector // European Conference on Computer Vision. Amsterdam, 2016: 21
[15]	Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Amsterdam, 2016: 779
[16]	Law H, Deng J. CornerNet: detecting objects as paired keypoints // Proceedings of the European Conference on Computer Vision. Munich, 2018: 734
[17]	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, 2014: 580
[18]	Girshick R. Fast R-CNN // Proceedings of the IEEE International Conference on Computer Vision. Santiago, 2015: 1440
[19]	Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks // Advances in Neural Information Processing Systems. Montreal, 2015: 91
[20]	He K M, Gkioxari G, Dollár P, et al. Mask R-CNN // Proceedings of the IEEE International Conference on Computer Vision. Honolulu, 2017: 2961
[21]	Huang R, Pedoeem J, Chen C X. YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers // 2018 IEEE International Conference on Big Data (Big Data). Seattle, 2018: 2503
[22]	Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J/OL]. arXiv preprint (2017-04-17)[2019-09-15]. https://arxiv.org/abs/1704.04861
[23]	Zhang X Y, Zhou X Y, Lin M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 6848
[24]	Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J/OL]. arXiv preprint (2016-01-07)[2019-09-15]. https://arxiv.org/abs/1511.06434
[25]	Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, 2017: 2117