Concrete Cracks Detection Based on Deep Learning Image Classification

Abstract

Abstract:This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network (CNN) image classification algorithm. Provided a relatively heterogeneous dataset, the use of deep learning enables the development of a concrete cracks detection system that can account for several conditions, e.g., different light, surface finish and humidity that a concrete surface might exhibit. These conditions are a limiting factor when working with computer vision systems based on conventional digital image processing methods. For this work, a dataset with 3500 images of concrete surfaces balanced between images with and without cracks was used. This dataset was divided into training and testing data at an 80/20 ratio. Since our dataset is rather small to enable a robust training of a complete deep learning model, a transfer-learning methodology was applied; in particular, the open-source model VGG16 was used as basis for the development of the model. The influence of the model's parameters such as learning rate, number of nodes in the last fully connected layer and training dataset size were investigated. In each experiment, the model's accuracy was recorded to identify the best result. For the dataset used in this work, the best experiment yielded a model with accuracy of 92.27%, showcasing the potential of using deep learning for concrete crack detection.

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

“…In a similar vein, Silva et al [32] developed a concrete crack detection system based on deep learning using transfer learning schema. The proposed system used the pre-trained VGG16 deep learning CNN model.…”

“…Standard CNN consists of several convolutional layers, pooling layers, and fully-connected (FC) layers. The main aim of the CNN is the automatic and adaptive learning of spatial hierarchies of useful features, from low-to highlevel patterns [32], [36], [42], [43]. Table 3 describes the different CNN layers.…”

Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning

“…There are also the studies by Minami et al (2019aMinami et al ( , 2019b, in which they improved the performance of photographic devices and performed the CNN. Other studies on crack detection using CNNs have also been conducted by, for example, Cha et al (2017Cha et al ( , 2018, Silva and Lucena (2018), Xue and Li (2018), Deng et al (2019), Jiang and Zhang (2019), Li and Zhao (2019) and Zhang et al (2019). Although several crack detection techniques using deep learning have been proposed so far as above, practical development is still in the early stage, since the technology itself is still immature.…”

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

“…A large number of previous works have particularly focused on detecting cracks in concrete structures [3][4][5]. e reason is that cracks are a major concern when considering the safety, durability, and serviceability of structures [1,6].…”

“…Besides edge detection and image filtering approaches, machine learning has also been successfully employed in concrete surface crack detection [8][9][10]. Particularly, convolutional neural network (CNN) models have drawn attention of many scholars in constructing automatic crack recognition models [2,4,19,20].…”