Dr. Tian Pu Profile

Dr. Tian Pu

at UESTC

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Proceedings Article | 8 February 2019 Paper

Missing pins detection for power equipment firmware using unmanned aerial vehicle images

Bingxin Huai, Ruiling Wang, Yuhan Liu, Li Song, Zhenming Peng

Proceedings Volume 10843, 108430S (2019) https://doi.org/10.1117/12.2506338

KEYWORDS: Unmanned aerial vehicles, Convolutional neural networks, Inspection equipment, Neural networks, Convolution, Feature extraction

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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.

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