Transmission line route tree distance detection technology based on the fusion of Laplace pyramid LUNet network

Gang Liu; Jian Wu; Xin Tao; Bocheng Li; Lei Qiao

doi:10.1117/12.3034387

16 October 2024 Transmission line route tree distance detection technology based on the fusion of Laplace pyramid LUNet network

Gang Liu, Jian Wu, Xin Tao, Bocheng Li, Lei Qiao

Author Affiliations +

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132911E (2024) https://doi.org/10.1117/12.3034387
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

The distance measurement method based on deep learning for monocular vision transmission line inspection unmanned aerial vehicle(UAV) has the advantages of low cost, high efficiency, and intelligence. To guide the dense encoding information at different scales more effectively and address the issue of image clarity reduction due to the loss of high-frequency information during upsampling, this article proposes a Laplace U-Net (LUNet) network architecture. The architecture enhances the global information decoding capability with the proposed LU module, introduces a Laplacian pyramid residual structure in each reconstruction module, and utilizes the encoding information from different depths of the encoder to guide the reconstruction of high-quality depth images. The experimental results show that the relative error of the new method is less than 6%, which provides technical support for the inspection UAV to carry out the distance detection technology of the transmission line and the tree conveniently through the monocular camera.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gang Liu, Jian Wu, Xin Tao, Bocheng Li, and Lei Qiao "Transmission line route tree distance detection technology based on the fusion of Laplace pyramid LUNet network", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132911E (16 October 2024); https://doi.org/10.1117/12.3034387

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KEYWORDS

RGB color model

Image processing

Depth maps

Inspection

Deep learning

Error analysis

Image restoration

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