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• W3064281161 endingPage "022051" @default.
• W3064281161 startingPage "022051" @default.
• W3064281161 abstract "With the construction and development of Chinese power grid, the scale of the power grid continues to expand, the transmission corridors environment is becoming more and more complex, the possibility of external damage caused by intrusion objects of the transmission line is constantly increasing, which seriously threatens the normal operation of the power grid and the reliability of power supply. In order to solve the above problems, it is particularly important to study a detection method that can replace manual identification. That method helps the field personnel to timely detect the intrusion objects within the monitoring scope of the transmission line, so as to improve the efficiency and accuracy of transmission channel inspection." @default.
• W3064281161 created "2020-08-24" @default.
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• W3064281161 date "2020-07-01" @default.
• W3064281161 modified "2023-09-24" @default.
• W3064281161 title "Intrusion Object Detection on Transmission Corridors Based on Deep Learning" @default.
• W3064281161 cites W2570343428 @default.
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• W3064281161 doi "https://doi.org/10.1088/1742-6596/1601/2/022051" @default.
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