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This study develops an improved computer vision and machine learning approach to categorize people’s attire in images and videos, with adaptability and a user-friendly graphical interface for system management and deployment. A unique mixed Western and Arabic clothing dataset is generated, incorporating privacy-focused measures. DeepFashion, People Segment, and web sources are used, including a labeled 1,520-image dataset. Using YOLOv8, CVAT-based annotation, and training in PyCharm/Colab, the model achieves F1-score 0.83 and mAP 0.84 for identifying appropriate versus inappropriate clothing.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/dress-code-violation-detection-in-arabic-regions-using-object-detection-machine-learning-model-master-thesis-defense/124959/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/dress-code-violation-detection-in-arabic-regions-using-object-detection-machine-learning-model-master-thesis-defense/124959.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-28","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the thesis address?","Question",{"text":113,"@type":114},"It addresses automated detection of dress-code violations to assess whether clothing in public images or videos is appropriate or inappropriate.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which model and tools are used for clothing detection?",{"text":118,"@type":114},"The study uses the YOLOv8 object detection model; images are annotated and bounding boxes are defined using the CVAT tool, with training performed in PyCharm and Google Colab.",{"name":120,"@type":111,"acceptedAnswer":121},"What datasets and results are reported?",{"text":122,"@type":114},"It leverages DeepFashion, the People Segment Dataset, and web sources, and reports an F1-score of 0.83 and mAP of 0.84 based on systematic evaluation using YOLOv8.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},124959,1785895628,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","The College of Graduate Studies and the College of Information Technology Cordially Invite You to  \nMaster Thesis Defense  \nEntitled  \nDRESS-CODE VIOLATION DETECTION IN ARABIC REGIONS USING OBJECT DETECTION MACHINE  \nLEARNING MODEL  \nby  \nMaha Sadat Sayyed Mahdi Aghaei  \nFaculty Advisor  \nDr. Munkhjargal Gochoo  \nCollege of Information Technology  \nDate & Venue  \nWednesday, 15 November 2023  \n9:00 AM  \nRoom 036, F3-Building  \nAbstract  \nThe dress code violation detection system is crucial for assessing clothing appropriateness in public areas. This study aims to improve this system using advanced computer vision and machine learning techniques to more effectively categorize people's attire in images and videos. To enhance adaptability and create a user-friendly graphical interface for system management and deployment, we have generated a unique dataset from various contexts mix of Western and Arabic clothing. This allows users to interact with graphical components, including the ability to upload images or use live video for clothing detection. Moreover, we have taken privacy concerns into account and implemented robust privacy measures. For this research, we utilized data from the DeepFashion dataset, the People Segment Dataset, and various web sources. Specifically for clothing detection, we've made a significant contribution by creating a labeled dataset containing 1520 images representing diverse clothing styles. You can access this Dress Code dataset publicly through [insert link] . We conducted a systematic review of dress code identification using the latest YOLOv8 machine learning model to distinguish between proper and improper clothing choices. To annotate images and define bounding boxes, we used the CVAT tool (Computer Vision Annotation Tool) and then trained the dataset using YOLOv8 in PyCharm and Google Colab. Our testing results showed an F1-score of 0.83 and an mAP of 0.84. The system is capable of identifying appropriate and inappropriate attire, whether through camera inputs or image uploads, powered by the latest YOLOv8 model. These findings underscore the technology's potential to redefine dress code enforcement and monitoring, providing a more efficient and accurate means of ensuring compliance.  \nKeywords: YOLO, Machine Learning, Dress Code, Appropriate, Inappropriate.","cbCaidGBbkOoybz2","https://ap.wps.com/l/cbCaidGBbkOoybz2","pdf",774146,"English","# Abstract\n## Dataset construction\n## Annotation and training\n## Evaluation results\n## System application","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses automated detection of dress-code violations to assess whether clothing in public images or videos is appropriate or inappropriate.\"},{\"question\":\"Which model and tools are used for clothing detection?\",\"answer\":\"The study uses the YOLOv8 object detection model; images are annotated and bounding boxes are defined using the CVAT tool, with training performed in PyCharm and Google Colab.\"},{\"question\":\"What datasets and results are reported?\",\"answer\":\"It leverages DeepFashion, the People Segment Dataset, and web sources, and reports an F1-score of 0.83 and mAP of 0.84 based on systematic evaluation using YOLOv8.\"}]","DRESS-CODE VIOLATION DETECTION IN ARABIC REGIONS USING OBJECT DETECTION MACHINE LEARNING MODEL - Master Thesis Defense | PDF"]