Core Insights - The Tianjin Electric Power Company has developed a high-quality sample set for transmission image monitoring, enhancing the AI platform data for the Tianjin power grid and improving the intelligent operation and maintenance level of transmission channels [1] - The sample set includes over 230,000 images of transmission towers and lines, and 170,000 images of cable channels, providing a comprehensive data foundation for the intelligent monitoring model [1] - The AI team addressed the previous limitations in model generalization due to a lack of high-quality and rare hazard samples, which restricted the large-scale application of the intelligent inspection system [1] Group 1 - The sample set integrates multi-source operation and maintenance data from the Tianjin power grid, supporting the autonomous training and iteration of intelligent monitoring models [1] - The AI team spent six months developing the sample set, focusing on the actual needs of intelligent operation and maintenance of transmission lines [1] - The integration of image resources from two operational platforms, the panoramic platform and the refined management platform, was crucial for achieving accurate data [1] Group 2 - To ensure image annotation quality, the AI team established a four-step process: "collection—cleaning—annotation—review," enhancing the standardization of the annotation platform [2] - The team implemented multi-level human intervention and review mechanisms, along with a calibration sample embedding mechanism to dynamically assess the quality of the workflow [2] - Continuous optimization of annotation quality is a key focus of the team's efforts [2]