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    Convolutional Neural Networks–Based Model for Automated Sewer Defects Detection and Classification

    Source: Journal of Water Resources Planning and Management:;2021:;Volume ( 147 ):;issue: 007::page 04021036-1
    Author:
    Qianqian Zhou
    ,
    Zuxiang Situ
    ,
    Shuai Teng
    ,
    Gongfa Chen
    DOI: 10.1061/(ASCE)WR.1943-5452.0001394
    Publisher: ASCE
    Abstract: Automated detection and classification of sewer defects can complement the conventional labor-intensive sewer inspection process by providing an essential tool to classify sewer defects in a more efficient, accurate, and consistent way. This paper presents a convolutional neural networks (CNNs)–based model to automatically detect and classify six most commonly observed sewer defects (i.e., cracks, disjoints, obstacles, residential walls, tree roots, and normal categories) obtained from multisource CCTV images under various circumstances. Data augmentation techniques (including geometric and color transformations) are applied to enhance the model performance. The proposed CNN model is further compared with a state-of-the-art solution (retraining the SqueezeNet using defect images) by adopting transfer learning technique. An average prediction accuracy of 90% is achieved, indicating that the investigated defects can be well recognized by the model without any expert knowledge of sewer detection. There is a higher degree of confidence in predicting tree roots and disjoints, followed by residential walls and cracks. Results show that the prediction accuracy has increased by 15% thanks to data augmentation. Despite the transferred SqueezeNet model achieved a higher accuracy (95%), it cost almost 13 times the computation time of the CNN model. The study demonstrates the feasibility of the deep learning technology in the automated classification of sewer defects and advances the knowledge in the research field.
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      Convolutional Neural Networks–Based Model for Automated Sewer Defects Detection and Classification

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4270623
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    contributor authorQianqian Zhou
    contributor authorZuxiang Situ
    contributor authorShuai Teng
    contributor authorGongfa Chen
    date accessioned2022-01-31T23:56:50Z
    date available2022-01-31T23:56:50Z
    date issued7/1/2021
    identifier other%28ASCE%29WR.1943-5452.0001394.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4270623
    description abstractAutomated detection and classification of sewer defects can complement the conventional labor-intensive sewer inspection process by providing an essential tool to classify sewer defects in a more efficient, accurate, and consistent way. This paper presents a convolutional neural networks (CNNs)–based model to automatically detect and classify six most commonly observed sewer defects (i.e., cracks, disjoints, obstacles, residential walls, tree roots, and normal categories) obtained from multisource CCTV images under various circumstances. Data augmentation techniques (including geometric and color transformations) are applied to enhance the model performance. The proposed CNN model is further compared with a state-of-the-art solution (retraining the SqueezeNet using defect images) by adopting transfer learning technique. An average prediction accuracy of 90% is achieved, indicating that the investigated defects can be well recognized by the model without any expert knowledge of sewer detection. There is a higher degree of confidence in predicting tree roots and disjoints, followed by residential walls and cracks. Results show that the prediction accuracy has increased by 15% thanks to data augmentation. Despite the transferred SqueezeNet model achieved a higher accuracy (95%), it cost almost 13 times the computation time of the CNN model. The study demonstrates the feasibility of the deep learning technology in the automated classification of sewer defects and advances the knowledge in the research field.
    publisherASCE
    titleConvolutional Neural Networks–Based Model for Automated Sewer Defects Detection and Classification
    typeJournal Paper
    journal volume147
    journal issue7
    journal titleJournal of Water Resources Planning and Management
    identifier doi10.1061/(ASCE)WR.1943-5452.0001394
    journal fristpage04021036-1
    journal lastpage04021036-10
    page10
    treeJournal of Water Resources Planning and Management:;2021:;Volume ( 147 ):;issue: 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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