Abstract
Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.
Highlights
Concrete structures in various forms, such as high-rise buildings, bridges and dams, suffer from continuous health deterioration during long service periods [1,2], making real-time detectionSensors 2020, 20, 2021; doi:10.3390/s20072021 www.mdpi.com/journal/sensorsSensors 2020, 20, 2021 of different types of structural damage a crucial demand [3]
With rapid development in recent years, artificial intelligence (AI) methods have been increasingly applied in structural damage detection [22], where crack recognition based on convolutional neural networks (CNNs) has shown promise in engineering applications [23]
In recognition of the influence of severe noise included in concrete surface images, which largely degraded the accuracy of existing methods in crack identification, a multi-layered image preprocessing strategy (MLP)–CNN framework was established in this paper relying on hybrid utilization of CNN and a multi-layered preprocessing technique, the key elements of which were homomorphic filtering and the Otsu thresholding method
Summary
Concrete structures in various forms, such as high-rise buildings, bridges and dams, suffer from continuous health deterioration during long service periods [1,2], making real-time detection. The accuracy of the aforementioned algorithms is still considered limited in crack identification when taking into account severe noise influences that are common in actual damage detection scenarios. With rapid development in recent years, AI methods have been increasingly applied in structural damage detection [22], where crack recognition based on convolutional neural networks (CNNs) has shown promise in engineering applications [23]. Compared to conventional machine learning methods, CNNs are powerful in learning the characteristics of images using a simpler network structure [24] Leveraging this merit, CNN-based methods can identify cracks with high efficiency, especially when dealing with multi-classification [25] and large-scale problems [26].
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