Local denoising method and device for oil level image of edge side transformer

By employing a local denoising method for transformer oil level images on the edge side, and utilizing BN neural networks and HSI spatial transformation, the problems of manual inspection and high hardware costs in transformer oil level detection are solved, achieving efficient and accurate oil level detection and timely feedback.

CN121883286APending Publication Date: 2026-04-17ZHEJIANG GONGSHANG UNIVERSITY
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Patent Information

Application Number
CN202310411596.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2023-04-13
Publication Date
2026-04-17

AI Technical Summary

Technical Problem

In existing technologies, transformer oil level detection relies on manual inspection, which has problems of high labor costs and low accuracy. Furthermore, image vision-based detection methods require powerful hardware resources and high-cost backend computing, and cannot provide timely feedback on oil level anomalies caused by environmental interference.

Method used

A local denoising method for transformer oil level images on the edge side is adopted. The BN neural network model is combined with HSI spatial transformation and multi-core processor to perform image preprocessing and extract the brightness component, reduce background interference, and reduce the amount of computation. Image acquisition and preprocessing are completed by edge side devices, and subsequent recognition is completed by the background computer.

Benefits of technology

Local image preprocessing improves processing efficiency, reduces data bandwidth overhead and costs, and enables efficient, accurate and timely feedback for transformer oil level detection.

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Abstract

The invention relates to the technical field of image processing, solves the problems that in the prior art, a wide wireless channel is needed for data transmission of a background computer, and the requirement for the operation performance of the background computer is high, and discloses an edge side transformer oil level image local denoising method and device, and the method comprises the steps: obtaining an original image of a transformer oil level; constructing a BN neural network model; carrying out HSI spatial transformation on the original image to independently extract a brightness component Ix; performing convolution processing and nonlinear mapping on the brightness component Ix by using the BN neural network model, and performing feature learning to obtain residual brightness F (IX); superposing the brightness component IX and the residual brightness F (IX) to obtain a reconstructed image; according to the method, image preprocessing is locally completed, so that the processing efficiency is relatively high, the original data volume is reduced, and the data bandwidth overhead and cost are saved.
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