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Missing pins detection for power equipment firmware using unmanned aerial vehicle images
Author(s): Bingxin Huai; Ruiling Wang; Yuhan Liu; Li Song; Zhenming Peng
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Paper Abstract

Maintenance of power equipment is of great significance to ensure the safety and reliability of power equipment. This paper focuses on detecting missing pins of power equipment using Unmanned Aerial Vehicle (UAV) acquired images. We proposed a detection method based on image color histogram and scale invariant feature transform (SIFT). The first step calculates the H-S color histogram of screw image, utilizing histogram back projection method to obtain candidate regions of screw image in the to-be-matched image, applied Bhattacharyya distance as a measurement to compare the similarity of two histograms. Then, the SIFT feature is extracted from the screw image and the key points are matched with the SIFT feature of the candidate regions to detect the screws. Finally, this paper designs a method which uses convolutional neural network to judge whether the screw misses the pin. Experiments show that the proposed algorithm of missing pins detection based on UAV image can achieve competitive results to detect the defects in special scenes, and has good robustness, which satisfies the real-time and accuracy requirements.

Paper Details

Date Published: 8 February 2019
PDF: 7 pages
Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108430S (8 February 2019); doi: 10.1117/12.2506338
Show Author Affiliations
Bingxin Huai, Univ. of Electronic Science and Technology of China (China)
Ruiling Wang, Univ. of Electronic Science and Technology of China (China)
Yuhan Liu, Univ. of Electronic Science and Technology of China (China)
Li Song, Univ. of Electronic Science and Technology of China (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10843:
9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging
Yadong Jiang; Xiaoliang Ma; Xiong Li; Mingbo Pu; Xue Feng; Bernard Kippelen, Editor(s)

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