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- W4387192727 abstract "In order to improve the intelligence level of power grid operation and enhance the credibility of rust defect detection in power equipment, this paper addresses the significant differences in image quality in actual situations of unmanned aerial vehicle (UAV) inspection. A rust detection method incorporating an attention mechanism into the Faster R-CNN model and considering background brightness and gamma transformation is proposed. This method can adaptively normalize the images and effectively identify rust areas in complex backgrounds, large brightness differences, and noise-contaminated conditions. By introducing three assessment indicators: rust rate, rust confidence, and rust heat map, comprehensive rust defect information is provided to maintenance personnel, ensuring the safe and stable operation of power equipment." @default.
- W4387192727 created "2023-09-30" @default.
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- W4387192727 date "2023-07-21" @default.
- W4387192727 modified "2023-10-01" @default.
- W4387192727 title "Research on the Application of Deep Learning Object Detection in Rust Defect Detection of Power Equipment" @default.
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- W4387192727 doi "https://doi.org/10.1109/iceemt59522.2023.10263152" @default.
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