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Automatic detection of cracks in concrete surfaces based on image processingis a clear trend in modern civil engineering applications. Most infrastructureis made of concrete and cracks reveal degradation of the structural integrityof the facilities, which can lead to extreme structural failures. There aremany approaches to overcome the difficulties in image-based crack detection,ranging from the pre-processing of the input image to the proper adjustment ofefficient classifiers, passing through the essential feature selection step.This paper is related to the process of constructing features from images toallow a classifier to find the boundaries between images with and withoutcracks. The most common approaches to feature extraction are the convolutionaltechniques to extract relevant positional information from images and thefilters for edge detection or background removal. Here we apply hashingtechniques for the first time used for features extraction in this problem. Thestudy of the classification capacity of hashes is carried out by comparing 5different hash algorithms, 2 of which are based on wavelets. The effect ofapplying the z-transform on the images before calculating the hashes was alsostudied, which totals the study of 10 new features for this problem. Acomparative study of 17 different algorithms from the scikit-learn library wascarried out. The results show that 9 of the 10 features are relevant to theproblem, as well as that the accuracy of the classifiers varied between 0.697for the Naive-Bayes Gaussian classifier and 0.99 for the Random Forest (RF)classifier. The feature extraction algorithm developed in this work and the RFclassifier algorithm is suitable for embedded applications, for example ininspection drones, as long as they are highly accurate and computationallylight, both in terms of memory and processing time.

A High Accuracy Image Hashing and Random Forest Classifier for Crack Detection in Concrete Surface Images