[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125748-en":3,"doc-seo-125748-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"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":14,"update_tm":28,"read_time":29},125748,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning and Computer Vision for Urban Drainage Inspections","Machine Learning and Computer Vision for Urban Drainage Inspections presents a research framework for sewer asset management, integrating machine learning methods with digital image processing to analyze drainage inspection imagery. It develops an unsupervised anomaly detection approach using PCA-based decomposition, feature descriptors, and dissimilarity functions, followed by application to sewer pipe images. The work further introduces convolutional neural network classification with multi-label loss, handling class imbalance, cross-validation strategy, implementation details, and reported results, supported by an end-to-end methodological evaluation.","Machine learning and computer vision for urban drainage inspections  \nMeijer, D.W.J.  \nCitation  \nMeijer, D. W. J. (2023, November 7). Machine learning and computer vision for urban drainage inspections. Retrieved from [https://hdl.handle.net/1887/3656056](https://hdl.handle.net/1887/3656056)  \nVersion: Publisher's Version  \nLicence agreement concerning inclusion of doctoral  \nLicense:  thesis in the Institutional Repository of the University  \nof Leiden  \nDownloaded from:  [https://hdl.handle.net/1887/3656056](https://hdl.handle.net/1887/3656056)  \n[Note:](Note: To cite this publication please use the final published version)[ To cite this publication please use the final published version](Note: To cite this publication please use the final published version) (if applicable) .  \nMachine Learning and Computer Vision for Urban Drainage Inspections  \n“What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking—there’s the real danger.”  \nFrank Herbert, God Emperor of Dune  \nMachine Learning and Computer Vision for Urban Drainage Inspections  \nProefschrift  \nter verkrijging van  \nde graad van doctor aan de Universiteit Leiden, [op gezag van rector magnificus prof.dr.ir. H. Bijl](op gezag van rector magnificus prof.dr.ir. H. Bijl), volgens besluit van het college voor promoties te verdedigen op dinsdag 7 november 2023 klokke 15.00 uur  \ndoor  \nDirk Willem Johannes Meijer geboren te ‘s-Gravenhagein 1989  \nPromotores:  \ndr. A. J. Knobbe  \nprof.dr. T. H. W. Bck  \nPromotiecommissie:  \nprof.dr. M. S. K. Lew  \n[prof.dr.ir. F. J. Verbeek](prof.dr.ir. F. J. Verbeek)  \n[dr. L. Scholten](dr. L. Scholten) ([TU Delft](TU Delft))  \n[prof.dr.ir. F. H. L. R. Clemens](prof.dr.ir. F. H. L. R. Clemens) (TU Delft, NTNU Trondheim)  \ndr. K. J. Wolstencroft  \nThis work is part of the Cooperation Programme TISCA (Technology Innovation for Sewer Condition Assessment) with project number 15343, which is (partly) financed by NWO domain TTW (the domain applied and Engineering Sciences of the Netherlands Organisation for Scientific Research), the RIONED Foundation, STOWA (Foundation for Applied Water Research) and the Knowledge Program Urban Drainage (KPUD) .  \nCover design by Dirk W. J. Meijer, from data collected by Rianne A. Luimes.  \nTypeset with pdfLATEX, style based on tufte-latex.  \nPrinted by NBD Biblion Services.  \nCopyright © 2023 Dirk W. J. Meijer.  \nDedicated to the memory ofIlundi for helping me rediscover meaning.  \nContents  \n1 Introduction ........................................ 11  \n1.1 Motivation ........................................... 11  \n1.1.1 Sewer Asset Management ........................... 11  \n1.1.2 Machine Learning and Computer Vision ................ 12  \n1.1.3 Scope .......................................... 13  \n1.2 Research Questions ..................................... 13  \n1.3 Contribution ......................................... 16  \n2 Preliminaries ........................................ 18  \n2.1 Machine Learning ...................................... 18  \n2.1.1 Classification .................................... 18  \n2.1.2 Regression ...................................... 19  \n2.1.3 Overfitting, Regularization, Cross Validation ............. 20  \n2.1.4 Model Validation ................................. 23  \n2.1.5 Anomaly Detection ............................... 27  \n2.1.6 Principal Component Analysis ....................... 28  \n2.2 Digital Image Processing ................................. 29  \n2.2.1 Convolution .................................... 29  \n2.3 Convolutional Neural Networks ........................... 31  \n2.3.1 The Perceptron .................................. 31  \n2.3.2 Convolutional layers ............................... 34  \n2.3.3 Pooling layers .................................... 35  \n2.3.4 Convolutional Neural Network Design ................. 36  \n2.4 Computer Stereovision .................................. 37  \n3 Image-Based U","cbCaip9mJpbFQxZN","https://ap.wps.com/l/cbCaip9mJpbFQxZN","pdf",349254,1,11,"English","en",105,"# Introduction\n## Motivation\n## Research Questions\n## Contribution\n# Preliminaries\n## Machine Learning\n## Digital Image Processing\n## Convolutional Neural Networks\n## Computer Stereovision\n# Image-Based Unsupervised Anomaly Detection\n## Framework\n## Proof of Concept\n## Application in Sewer Pipe Images\n## Convolutional Autoencoder\n## Summary\n# Convolutional Neural Network Classification\n## Introduction\n## Data Exploration\n## Methodology\n## Results\n## Discussion","[{\"question\":\"What problem does the research address in urban drainage inspections?\",\"answer\":\"It targets analysis of sewer/drainage inspection data for sewer asset management, enabling automated detection and classification using machine learning and computer vision.\"},{\"question\":\"How does the document perform unsupervised anomaly detection?\",\"answer\":\"It uses a PCA decomposition with partial reconstruction, feature descriptors, and a dissimilarity function to identify anomalies from image-based representations.\"},{\"question\":\"How is the classification approach structured and evaluated?\",\"answer\":\"It applies convolutional neural networks with multi-label loss, manages class imbalance via techniques such as oversampling, and evaluates performance using a leave-two-inspections-out cross-validation strategy and aggregation at the pipe level.\"}]","Machine Learning and Computer Vision for Urban Drainage Inspections | 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