[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-187586-en":3,"doc-seo-187586-105":30,"detail-sidebar-cat-1-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":4,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},187586,1374404730887,"Pentious","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",1,158,"General","Detection and Analysis of Defects on Game Cards","This document details the application of object detection models, specifically YOLOv5, for identifying and analyzing defects on game cards. The images showcase the YOLOv5 model's ability to precisely locate and classify 'Corner' and 'Edge Wear' defects on Pokémon cards. The accompanying infographic illustrates the different YOLOv5 model configurations (Nano, Small, Medium, Large, XLarge) along with their respective sizes, inference speeds, and accuracy metrics (mAPCOCO) on the COCO dataset. The analysis presented in the images indicates a high degree of accuracy in defect detection, with confidence scores annotated for each detected defect. This technology has significant implications for quality control in the trading card industry, enabling automated and efficient inspection processes. The models are capable of distinguishing between various types of wear and tear, providing valuable data for grading and authentication purposes. The comparative performance metrics of different YOLOv5 variants suggest that the optimal model choice depends on the trade-off between detection speed and accuracy requirements for specific applications, with larger models generally offering higher precision at the cost of increased computational resources.","","cbCailDp70z5wdrG","https://ap.wps.com/l/cbCailDp70z5wdrG","pdf",6887420,23,"English","en",105,"# YOLOv5 Model Architectures and Performance\n## YOLOv5n (Nano)\n## YOLOv5s (Small)\n## YOLOv5m (Medium)\n## YOLOv5l (Large)\n## YOLOv5x (XLarge)\n\n# Defect Detection on Game Cards\n## Corner Defects\n## Edge Wear Defects","[{\"question\":\"What types of defects are being detected on the game cards?\",\"answer\":\"The system is designed to detect 'Corner' defects and 'Edge Wear' on game cards, as demonstrated in the provided image analysis.\"},{\"question\":\"What object detection model is used for analyzing the game cards?\",\"answer\":\"The analysis utilizes the YOLOv5 object detection model, with various configurations like Nano, Small, Medium, Large, and XLarge being presented with their performance metrics.\"},{\"question\":\"What are the key performance metrics shown for the YOLOv5 models?\",\"answer\":\"The performance metrics include model size (MB), inference speed (ms), and accuracy (mAPCOCO), indicating the trade-offs between different model architectures.\"}]","Detection and Analysis of Defects on Game Cards | 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types of defects are being detected on the game cards?","Question",{"text":74,"@type":75},"The system is designed to detect 'Corner' defects and 'Edge Wear' on game cards, as demonstrated in the provided image analysis.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What object detection model is used for analyzing the game cards?",{"text":79,"@type":75},"The analysis utilizes the YOLOv5 object detection model, with various configurations like Nano, Small, Medium, Large, and XLarge being presented with their performance metrics.",{"name":81,"@type":72,"acceptedAnswer":82},"What are the key performance metrics shown for the YOLOv5 models?",{"text":83,"@type":75},"The performance metrics include model size (MB), inference speed (ms), and accuracy (mAPCOCO), indicating the trade-offs between different model 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