DOI: 10.5281/zenodo.6870107
title: Dataset for Crack Detection in Images of Bricks and Masonry Using CNNs

description: <strong>Dataset for training CNN built from aerial drone images of buildings in Hamburg</strong> This dataset contains images extracted from aerial surveillance photos of the Speicherstadt and Kesselhaus buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images. Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use): Concrete Crack Images for Classification (Mendeley Data) Dataset for Crack Detection in Images of Masonry Using CNNs Such a combined data set resulted in over 80 thousand of images. <strong>Matlab WebApp Server application based on trained SqueezeNet CNN </strong> The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by Kenta Itakura in his article published on Matlab Central: Classify crack image using deep learning and explain "WHY", which in turn is based on the work of Lei Zhang reported in his IEEE article: Road crack detection using deep convolutional neural network published at 2016 IEEE International Conference on Image Processing (ICIP). The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "<em>netTransfer.mat</em>" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at MATLAB Web App Server and Get Started with MATLAB Web App Server. After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at MATLAB Runtimes (mathworks.com). The important function included in the package is "unscramble.m", which <strong>corrects the error that exists in all known revisions of Matlab</strong> in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.
description: {"references": ["Classify crack image using deep learning and explain \"WHY\": https://www.mathworks.com/matlabcentral/fileexchange/75418-classify-crack-image-using-deep-learning-and-explain-why?s_tid=srchtitle", "Dataset for Crack Detection in Images of Masonry Using CNNs: https://zenodo.org/record/5108846"]}