[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-663943-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-663943-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","software-lab-symbol-detection-in-complex-technical-drawings","Software Lab - Symbol Detection in Complex Technical Drawings","","This project investigates the application of artificial intelligence for the automated detection and classification of symbols within complex technical drawings across engineering, architecture, and manufacturing sectors. By establishing a comprehensive, annotated dataset and evaluating advanced object detection architectures such as YOLO and Faster R-CNN, the research aims to streamline design interpretation and workflow automation. The documentation outlines key methodological stages, including data preprocessing, rigorous model training, and performance validation using precision, recall, and mean Average Precision metrics. It serves as a foundational guide for utilizing deep learning to enhance technical documentation accuracy and operational efficiency.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/technology/","Technology",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/software-lab-symbol-detection-in-complex-technical-drawings/663943/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/software-lab-symbol-detection-in-complex-technical-drawings/663943.png","ImageObject",300,407,{"name":42,"@type":43},"lilac","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":52,"interactionType":53,"userInteractionCount":22},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What is the primary goal of the symbol detection project?","Question",{"text":62,"@type":63},"The project aims to automate the recognition and classification of standardized symbols in technical drawings using artificial intelligence to improve workflow automation and design consistency.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What AI models are mentioned for potential use in this project?",{"text":67,"@type":63},"The document specifies object detection models such as YOLO and Faster R-CNN for training on the gathered technical drawing dataset.",{"name":69,"@type":60,"acceptedAnswer":70},"How will the performance of the trained models be evaluated?",{"text":71,"@type":63},"The model performance will be measured using metrics including precision, recall, and mAP (mean Average Precision).","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},663943,1793234985,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,104,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":102,"slug":103},6,50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":101,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":22,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":79,"read_time":30},3985747861865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Software Lab:  \nSymbol detection in complex technical drawings  \nModeling:   \nMathematics:   \nProgramming:   \nScience:   \nDescription  \nTechnical drawings are a universal medium for conveying detailed information across industries such as engineering, architecture, and manufacturing. These drawings rely on standardized symbols to represent components, connections, and processes. Symbols serve as the foundation for interpreting and implementing designs, making their accurate identification critical for automating workflows and ensuring consistency.  \nThis project focuses on symbol detection in complex technical drawings , aiming to automate the recognition and classification of symbols using Artificial Intelligence (AI) . The task involves the creation of a large dataset of diverse technical drawings, followed by testing and evaluating various AI models to identify the best-performing solution for accurate symbol detection.  \nTask  \n• Dataset Creation: Gather and label diverse technical drawings with symbol annotations.  \n• Data Preprocessing: Clean, enhance, and augment drawings for better model training.  \n• Model Training: Train object detection models (e.g. , YOLO, Faster R-CNN) on the dataset.  \n• Model Evaluation: Measure performance with precision, recall, and mAP metrics.  \n• Validation: Test the system on new drawings andrefine based on results.  \nSource: [1]  \nSupervisor  \nAndrea Carrara, Computational Modelling and Simulation chair, [andrea.carrara@tum.de](andrea.carrara@tum.de)  \nReferences  \n[1] Elyan, E., Jamieson, L., & Ali-Gombe, A. (2020). Deep learning for symbols detection and classification in engineering drawings. Neural networks, 129, 91-102.","cbCaijIw0Qv5SWkC","https://ap.wps.com/l/cbCaijIw0Qv5SWkC","pdf",430884,"English","# Project Overview\n## Task Phases\n## Project Resources and Supervision","[{\"question\":\"What is the primary goal of the symbol detection project?\",\"answer\":\"The project aims to automate the recognition and classification of standardized symbols in technical drawings using artificial intelligence to improve workflow automation and design consistency.\"},{\"question\":\"What AI models are mentioned for potential use in this project?\",\"answer\":\"The document specifies object detection models such as YOLO and Faster R-CNN for training on the gathered technical drawing dataset.\"},{\"question\":\"How will the performance of the trained models be evaluated?\",\"answer\":\"The model performance will be measured using metrics including precision, recall, and mAP (mean Average Precision).\"}]","Software Lab - Symbol Detection in Complex Technical Drawings | PDF"]