RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL) | Zendy

Weishan Zhang | Zendy; Xia Liu | Zendy; Jiangru Yuan | Zendy; Liang Xu | Zendy; Haoyun Sun | Zendy; Jiehan Zhou | Zendy; Xin Liu | Zendy

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RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)

Author(s) -

Weishan Zhang,

Xia Liu,

Jiangru Yuan,

Liang Xu,

Haoyun Sun,

Jiehan Zhou,

Xin Liu

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.01.232

Subject(s) - computer science , electric power transmission , transmission (telecommunications) , object detection , artificial intelligence , power (physics) , object (grammar) , pattern recognition (psychology) , computer vision , telecommunications , electrical engineering , physics , quantum mechanics , engineering

This paper proposes a new deep learning network - RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable for detecting foreign objects on power transmission lines. The RCNN4SPTL uses RPN (Region Proposal Network) to generate aspect ratio of the region proposals to align with the size of foreign objects. The RCNN4SPTL uses an end to end training to improve its performance. Experimental results show that the RCNN4SPTL significantly improves the detection speed and recognition accuracy, compared with the original Faster RCNN.

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