Piping is an essential component in buildings, and its as-built information is critical to facility management tasks. Manually extracting piping information from legacy drawings that are in paper, PDF, or image format is mentally exerting, time-consuming, and error-prone. Symbol recognition and detection are core problems in the computer-based interpretation of piping drawings, and the main technical challenge is to determine robust features that are invariant to scaling, rotation, and translation. This thesis aims to use convolutional neural networks CNNs to automatically extract features from raw images, and consequently, to locate and recognize symbols in piping drawings.
CNN-based Symbol Recognition and Detection in Piping Drawings
Cnn Master's Thesis Example - Writing a Thesis on Cnn Thesis Analysis
London CNN Business Black women with natural hairstyles including curly afros, twists or braids are less likely to get job interviews than White women or Black women with straightened hair, new research shows. More Videos Does posting BLM signs help or hurt businesses? Watch Consumer Reports trick Tesla's Autopilot system. Weekly jobless claims fall to pandemic low.
Black women with natural hairstyles are less likely to get job interviews
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