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Article

Deep Learning Based Electric Pylon Detection in Remote Sensing Images

by 1, 1 and 1,2,3,*
1
Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 102206, China
2
Beijing Key Laboratory of Digital Media, Beijing 102206, China
3
Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1857; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111857
Received: 5 May 2020 / Revised: 28 May 2020 / Accepted: 5 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
The working condition of power network can significantly influence urban development. Among all the power facilities, electric pylon has an important effect on the normal operation of electricity supply. Therefore, the work status of electric pylons requires continuous and real-time monitoring. Considering the low efficiency of manual detection, we propose to utilize deep learning methods for electric pylon detection in high-resolution remote sensing images in this paper. To verify the effectiveness of electric pylon detection methods based on deep learning, we tested and compared the comprehensive performance of 10 state-of-the-art deep-learning-based detectors with different characteristics. Extensive experiments were carried out on a self-made dataset containing 1500 images. Moreover, 50 relatively complicated images were selected from the dataset to test and evaluate the adaptability to actual complex situations and resolution variations. Experimental results show the feasibility of applying deep learning methods to electric pylon detection. The comparative analysis can provide reference for the selection of specific deep learning model in actual electric pylon detection task. View Full-Text
Keywords: electric pylon detection; deep learning; remote sensing image electric pylon detection; deep learning; remote sensing image
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MDPI and ACS Style

Qiao, S.; Sun, Y.; Zhang, H. Deep Learning Based Electric Pylon Detection in Remote Sensing Images. Remote Sens. 2020, 12, 1857. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111857

AMA Style

Qiao S, Sun Y, Zhang H. Deep Learning Based Electric Pylon Detection in Remote Sensing Images. Remote Sensing. 2020; 12(11):1857. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111857

Chicago/Turabian Style

Qiao, Sijia, Yu Sun, and Haopeng Zhang. 2020. "Deep Learning Based Electric Pylon Detection in Remote Sensing Images" Remote Sensing 12, no. 11: 1857. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111857

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