  Automatic detection of cracks in concrete surfaces based on image processing
is a clear trend in modern civil engineering applications. Most infrastructure
is made of concrete and cracks reveal degradation of the structural integrity
of the facilities, which can lead to extreme structural failures. There are
many approaches to overcome the difficulties in image-based crack detection,
ranging from the pre-processing of the input image to the proper adjustment of
efficient classifiers, passing through the essential feature selection step.
This paper is related to the process of constructing features from images to
allow a classifier to find the boundaries between images with and without
cracks. The most common approaches to feature extraction are the convolutional
techniques to extract relevant positional information from images and the
filters for edge detection or background removal. Here we apply hashing
techniques for the first time used for features extraction in this problem. The
study of the classification capacity of hashes is carried out by comparing 5
different hash algorithms, 2 of which are based on wavelets. The effect of
applying the z-transform on the images before calculating the hashes was also
studied, which totals the study of 10 new features for this problem. A
comparative study of 17 different algorithms from the scikit-learn library was
carried out. The results show that 9 of the 10 features are relevant to the
problem, as well as that the accuracy of the classifiers varied between 0.697
for the Naive-Bayes Gaussian classifier and 0.99 for the Random Forest (RF)
classifier. The feature extraction algorithm developed in this work and the RF
classifier algorithm is suitable for embedded applications, for example in
inspection drones, as long as they are highly accurate and computationally
light, both in terms of memory and processing time.
