Unplanned construction operation on the site of power transmission and transformation project occurs frequently. In order to catch up with or speed up the construction progress, the construction site does not report the plan and arranges the operation without permission. This kind of unplanned operation lacks monitoring and is untracked, which inevitably leads to inadequate safety measures, leading to greater construction safety risks. Satellite remote sensing technology has developed rapidly. It has significant advantages such as wide coverage, a stable renewal cycle, less limited by environmental conditions, and a large amount of information. At the same time, it is completely independent of the construction group. It can provide objective and full coverage visualization of the transmission line construction project. It is an effective technical means to carry out unplanned operation investigations and random inspections.
As an important part of the transmission channel, the intelligent monitoring of the construction operation status of the transmission pole tower is an important research direction of the intelligent inspection of the transmission channel. The development of artificial intelligence technologies such as deep learning provides good conditions for the intelligent monitoring of the construction status of transmission poles and towers. However, the application of deep learning models requires high-quality sample data. At present, there is still a small sample library of the construction status of transmission poles and towers. The construction of a set of transmission pole tower construction operation status sample database that can be used for deep learning model is of positive significance for intelligent inspection of transmission channels. This study focuses on the characteristics of satellite remote sensing images under different operating conditions, compares the remote sensing images with real images, and gives a detailed process for constructing a sample library for monitoring construction operation status. It expands the innovative application of artificial intelligence in the intelligent inspection of transmission channels, and further improves the modernization level of construction operation monitoring and supervision.
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