KEYWORDS: Point clouds, LIDAR, Data transmission, Inspection, 3D modeling, Remote sensing, Data modeling, Optical transmission, 3D image processing, Laser scanners
Facing the extraction of hidden danger information of external breakage to transmission lines, we first introduce lidar and optical remote sensing image data acquisition, processing, and information extraction technologies, then analyze the characteristics of transmission corridors obtained by these two sensing methods, and summarize their respective advantages and disadvantages. On this basis, we propose a method for external breakage hidden danger information of transmission lines by combining remote sensing images and LiDAR point clouds. This method can simultaneously acquire the texture and spatial three-dimensional data of the external breakage target in the transmission corridor, and form high-quality spatial three-dimensional model data, which is conducive to the effective identification and accurate extraction of external breakage hidden danger.
Wind-induced deflection of overhead transmission line is a phenomenon of conductor non-synchronous swing caused by strong wind. Serious wind deflection will cause flashover and lead to line trip. It is of great significance to strengthen the monitoring of wind deflection of transmission line insulator. For transmission lines located in cold and strong wind areas, due to the need for on-site power supply, the long-term operation reliability of monitoring devices in low temperature environment is difficult to ensure. The monitoring scheme based on Fiber Bragg grating technology has the advantages of no on-site power supply, anti-electromagnetic interference and good insulation performance. It has a good application prospect in wind deflection monitoring of transmission lines in cold areas. This paper analyzes the characteristics of wind load, the causes of wind deflection of transmission line, and introduces the basic principle of measuring insulator inclination when using fiber Bragg grating sensor, which provides technical support for wind deflection monitoring of transmission line in cold areas and ensures the structural safety, safe and stable operation of transmission line.
KEYWORDS: Video, Data modeling, Performance modeling, Image enhancement, Video processing, Safety, Feature extraction, Inspection, Control systems, Algorithm development
A method for predicting abnormal behaviors of substation workers based on video scenes and using generative confrontation networks to integrate global and local information is proposed. In the substation, this method can be used to issue timely warnings to the transportation and inspection personnel that may trigger dangerous actions during the operation, so as to provide an important guarantee for the life and safety of the transportation and inspection personnel. The human behavior prediction task aims to predict future behavior video frames based on a given behavior video frame. Considering that the human behavior video contains not only relatively stable scene information, but also time-varying and complex human behavior information, this method first uses a global generation confrontation network to generate video scenes and rough human contours; then uses local generation confrontation Network to further optimize the details of human behavior in the video. Experiments show that, compared with the existing methods that only use a single model to achieve pixel-level behavior prediction, the method of combining global and local generation proposed in this paper can better capture the spatial appearance and the timing dynamics of humans in the video.
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