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src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79468980/Wall_Crack_Detection_Using_Transfer_Learning_based_CNN_Models">Wall Crack Detection Using Transfer Learning-based CNN Models</a></div><div class="wp-workCard_item"><span>2020 IEEE 17th India Council International Conference (INDICON)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. This paper aims to develop an automatic inspection system based on deep learning model and image processing to identify cracks. Transfer-learning models of convolutional neural networks (CNNs) are used to learn the intrinsic features of cracks using the images of the surfaces, which help them for the automatic classification into cracked/un-cracked classes. We have explored, MobileNetV2, ResNet101, VGG16 and InceptionV2 architectures of CNN model and presented a performance comparison analysis of these models for crack detection in this work. After evaluating our proposed approach of crack-detection on publicly available datasets, we have found that out of all the pre-trained CNN models MobileNet yields the best performance with 99.59% detection accuracy after 10-fold cross validation and outperforms the state-o...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79468980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79468980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79468980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79468980]").text(description); $(".js-view-count[data-work-id=79468980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79468980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79468980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79468980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79468980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79468980,"title":"Wall Crack Detection Using Transfer Learning-based CNN Models","translated_title":"","metadata":{"abstract":"Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. 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After evaluating our proposed approach of crack-detection on publicly available datasets, we have found that out of all the pre-trained CNN models MobileNet yields the best performance with 99.59% detection accuracy after 10-fold cross validation and outperforms the state-o...","publisher":"2020 IEEE 17th India Council International Conference (INDICON)","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"2020 IEEE 17th India Council International Conference (INDICON)"},"translated_abstract":"Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. This paper aims to develop an automatic inspection system based on deep learning model and image processing to identify cracks. 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After evaluating our proposed approach of crack-detection on publicly available datasets, we have found that out of all the pre-trained CNN models MobileNet yields the best performance with 99.59% detection accuracy after 10-fold cross validation and outperforms the state-o...","internal_url":"https://www.academia.edu/79468980/Wall_Crack_Detection_Using_Transfer_Learning_based_CNN_Models","translated_internal_url":"","created_at":"2022-05-19T12:42:51.924-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":206002325,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Wall_Crack_Detection_Using_Transfer_Learning_based_CNN_Models","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. This paper aims to develop an automatic inspection system based on deep learning model and image processing to identify cracks. Transfer-learning models of convolutional neural networks (CNNs) are used to learn the intrinsic features of cracks using the images of the surfaces, which help them for the automatic classification into cracked/un-cracked classes. We have explored, MobileNetV2, ResNet101, VGG16 and InceptionV2 architectures of CNN model and presented a performance comparison analysis of these models for crack detection in this work. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79468980"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79468980/Wall_Crack_Detection_Using_Transfer_Learning_based_CNN_Models"><img alt="Research paper thumbnail of Wall Crack Detection Using Transfer Learning-based CNN Models" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79468980/Wall_Crack_Detection_Using_Transfer_Learning_based_CNN_Models">Wall Crack Detection Using Transfer Learning-based CNN Models</a></div><div class="wp-workCard_item"><span>2020 IEEE 17th India Council International Conference (INDICON)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. This paper aims to develop an automatic inspection system based on deep learning model and image processing to identify cracks. Transfer-learning models of convolutional neural networks (CNNs) are used to learn the intrinsic features of cracks using the images of the surfaces, which help them for the automatic classification into cracked/un-cracked classes. We have explored, MobileNetV2, ResNet101, VGG16 and InceptionV2 architectures of CNN model and presented a performance comparison analysis of these models for crack detection in this work. After evaluating our proposed approach of crack-detection on publicly available datasets, we have found that out of all the pre-trained CNN models MobileNet yields the best performance with 99.59% detection accuracy after 10-fold cross validation and outperforms the state-o...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79468980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79468980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79468980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79468980]").text(description); $(".js-view-count[data-work-id=79468980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79468980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79468980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79468980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79468980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79468980,"title":"Wall Crack Detection Using Transfer Learning-based CNN Models","translated_title":"","metadata":{"abstract":"Early detection of cracks in building walls, roofs, bridges, etc. is quite important as these are early indicators for the ageing, decaying or any internal structural fault. 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