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

TL;DR

This paper introduces a novel image hashing feature extraction method combined with a Random Forest classifier for crack detection in concrete surfaces, achieving high accuracy suitable for embedded inspection devices.

Contribution

It pioneers the use of hashing techniques for feature extraction in concrete crack detection and compares multiple hash algorithms and classifiers for optimal performance.

Findings

Hash-based features are effective for crack detection.

Random Forest achieved up to 99% accuracy.

Proposed method is suitable for embedded applications.

Abstract

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.