[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126194-en":3,"doc-seo-126194-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126194,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Vehicle Registration-Plate Detection With Machine Learning - A Practical Approach","Vehicle registration plate detection thesis targeting limitations in the existing parking system (PERCS) and its third-party vehicle recognition product (VRS). PERCS introduced IoT surveillance cameras to send captured images to a vehicle recognition API, but many images lack clearly visible license plates, causing empty recognition results and extra monthly costs. The proposed solution identifies license-plate availability in images before API submission. The work selects and trains suitable machine learning models using PERCS images and deploys the implementation on an MS Azure environment.","Chaminda Kodithuwakku  \nVEHICLE REGISTRATION-PLATE DETECTION  \nWITH  \nMACHINE LEARNING  \nA Practical Approach  \nMaster Of Cloud Based Software Engineering  \nACKNOWLEDGEMENT  \nI would like to express my heartiest gratitude to my supervisor and our principal lecturer, Johan Dams. His continuous support, insightful feedback and invaluable guidance support me to deliver this thesis successfully.  \nI am also grateful to my previous working place. They allowed me to access their resources and gave me invaluable advice related to my thesis.  \nA special thanks to my wife, kids and friends for their encouragement at any time whenever I called.  \nFinally, I appreciate all the researchers, contributors in the Machine Learning and computer vision whose work inspired me and guided me to do this research.  \nThank you all for making this journey fruitful and filled with rewarding experiences.  \nChaminda Kumara Kodithuwakku  \n25th March 2025  \nVAASAN AMMATTIKORKEAKOULU UNIVERSITY OF APPLIED SCIENCES Cloud-Based Software Engineering  \nABSTRACT  \nAuthor Title  \nYear Language Pages  \nName of Supervisor  \nChaminda Kodithuwakku  \nVehicle Registration-Plate Detection With MLA Practical Approach  \n2024 English  \n73 + 6 Appendices Johan Dams  \nThe thesis about vehicle registration plate detection, which is based on the problem of the existing parking system, PERCS. The parking system uses a third-party product (VRS) for vehicle recognition. But PERCS stakeholders decided to replace the VRS system and introduce their own mechanism to recognize vehicles. As a result, surveillance cameras (as IoT) were introduced for parking areas to capture vehicles and send them to another API service to identify vehicle’s details. In that case, all the captured images are sent to a vehicle recognition API. Sometimes, license plates of many vehicles are not visible in the images. Therefore, the vehicle recognition API returns empty result when it sends vehicles without license plates. PERCS management wanted to ignore vehicles without license plates for image recognition API, as it incurred additional costs at the end of month.  \nTo address this issue, we decided to identify licence plate availability in the image before it sends to the vehicle recognition API. The task started with finding a suitable machine learning model. Also, required images to train the model are taken from PERCS system. This implementation hosts in MS Azure environment because PERCS systems are mostly hosted in Azure Web Apps.  \nKeywords License Plate Detection, Cloud-based Parking Systems, ML, IoT  \nCONTENTS  \nACKNOWLEDGEMENT ..................................................................................................2  \nLIST OF ABBREVIATIONS ..............................................................................................8  \n1 INTRODUCTION .................................................................................................. 11  \n2 PURPOSE, OBJECTIVES AND RESEARCH QUESTIONS/TASKS ........................... 12  \n2.1 Purpose ....................................................................................................... 12  \n2.2 Goals ............................................................................................................ 15  \n2.3 Research Questions/Tasks ......................................................................... 16  \n2.4 Outcomes .................................................................................................... 17  \n3 LITERATURE REVIEW .......................................................................................... 18  \n3.1 Overview of Vehicle Detection.................................................................. 18  \n3.2 Deep Learning in Vision Tasks................................................................... 19  \n3.3 Model comparison for Object Detection .................................................. 21  \n3.3.1 Faster R-CNN vs Retina NET .......................................","cbCaifoDACq7t9eb","https://ap.wps.com/l/cbCaifoDACq7t9eb","pdf",2266924,4,1,79,"English","en",105,"# Introduction\n# Purpose, Objectives and Research Questions/Tasks\n## Purpose\n## Goals\n## Research Questions/Tasks\n## Outcomes\n# Literature Review\n## Overview of Vehicle Detection\n## Deep Learning in Vision Tasks\n## Model comparison for Object Detection\n## Machine Learning Operations (MLOps)\n# Methodology\n## Identify the most suitable Surveillance Camera\n## Camera operation in PERCS\n## Dataset\n## Model training\n## Model evaluation\n# Deployment and Real-Time Testing\n## System Overview\n## Architecture Design of Milesight Integration\n## Proposed License Plate Detection Model Integration","[{\"question\":\"What problem does the thesis address in the PERCS parking system?\",\"answer\":\"The vehicle recognition API in PERCS often returns empty results when license plates are not visible, which also increases recognition costs. This happens when all captured images are sent to the API without checking plate availability.\"},{\"question\":\"What solution is proposed to reduce empty recognition results?\",\"answer\":\"The solution detects whether a license plate is available in an image before sending it to the vehicle recognition API. This requires selecting a suitable machine learning model and training it with PERCS images.\"},{\"question\":\"Where is the implementation deployed and why?\",\"answer\":\"The implementation is hosted in an MS Azure environment because PERCS systems are mostly hosted in Azure Web Apps. This supports integration and real-time testing for the detection workflow.\"}]","Vehicle Registration-Plate Detection With Machine Learning - A Practical Approach | PDF",1785903731,199,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"vehicle-registration-plate-detection-with-machine-learning-a-practical-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/vehicle-registration-plate-detection-with-machine-learning-a-practical-approach/126194/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in the PERCS parking system?","Question",{"text":76,"@type":77},"The vehicle recognition API in PERCS often returns empty results when license plates are not visible, which also increases recognition costs. This happens when all captured images are sent to the API without checking plate availability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What solution is proposed to reduce empty recognition results?",{"text":81,"@type":77},"The solution detects whether a license plate is available in an image before sending it to the vehicle recognition API. This requires selecting a suitable machine learning model and training it with PERCS images.",{"name":83,"@type":74,"acceptedAnswer":84},"Where is the implementation deployed and why?",{"text":85,"@type":77},"The implementation is hosted in an MS Azure environment because PERCS systems are mostly hosted in Azure Web Apps. 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