International Research Journal of Engineering and Technology (IRJET) Volume: 10 Issue: 05 | May 2023
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Crack Detection using Deep Learning Dr. Hansaraj Wankhede1, Abhishikth Thul2, Deep Malviya3, Pratik Pathe4, Shivam Kuite5 1,2,3,4,5 GHRCE Nagpur, India
------------------------------------------------------------------------***----------------------------------------------------------------------using image processing is now both affordable and effective Abstract— Concrete cracks are one of the primary [2][3].
signs of a structure that would cause major harm to the entire infrastructure. The conventional method of addressing cracks is manual inspection. We discovered during the survey that there are numerous existing technologies, including approaches based on computer vision and image processing. There are several techniques for locating fractures on different surfaces utilizing manual inputs. Different factors influenced the performance of traditional models, which provided results with variable degrees of accuracy. Because there are many different strategies that can be used to produce a good crack detection output, the performance and efficiency were influenced by the environment. Using a Convolutional Neural Network, we have constructed a deep learning model for fracture classification and segmentation. Convolution, activation, and pooling layers make up the foundation of convolutional neural networks. These layers enhance the performance and generalization of neural networks by extracting picture information, introducing nonlinearity, and reducing feature dimensionality. The observed model's accuracy was between 97 and 98 percent. This model can be used to keep track of the condition of the concrete surfaces of bridges, tunnels, and other public transportation infrastructure.
In practice, approaches for hand-crafted feature extraction from sub-windows based on intensity thresholding, edge detection, and other image processing techniques are frequently used [4].Although the efficiency of these systems in detecting pavement surfaces has been established, there is still room for development, particularly in the classification of different pavement fracture kinds. However, both computer vision and non-computer vision techniques have difficulties recovering fracture characteristics from pavement with shadows and complicated background pavement, as well as extracting cracks using low-level image cues [5]. Convolutional Neural Networks (CNN), a prominent tool of the artificial intelligence branch of Deep Learning (DL), have demonstrated their effectiveness in object detection. In contrast to conventional machine learning techniques, deep learning (DL) offers end-to-end classifiers that internally learn characteristics and can recognise objects automatically. The recent advancement of graphics processing units (GPU), which allows for extremely quick computations, together with this characteristic of DL algorithms have increased their use in a variety of sectors. In the instance of crack detection from images, the user need only supply various photos as input, and any detected cracks in these photos are returned as output without the need for any operator involvement [6]. Mendeley's Concrete Crack Images for is the source of the information in our model. Our deep learning model has a classification accuracy of between 97 and 98 percent.
Keywords— Crack detection, Deep Learning, Image Processing, Convolutional Neural Networks, Edge detection, Image Segmentation, Feature Extraction.
Introduction Finding surface cracks is crucial for maintaining infrastructure like roads and buildings. Using any of the processing approaches, crack detection is the process of locating cracks in structures. Early identification enables the implementation of preventative steps to stop potential failure and damage [1]. The conventional method required a lot of time and labor. By using a range of contemporary cameras to record crack images, it is possible to get beyond the lack of manual inspection and complete successful maintenance. The issue of fracture identification has recently been solved by modern technology, although it is not particularly precise because cracks can differ from structure to structure and be different on different surfaces. Accurately recognizing various types of cracks is also difficult because of the intricacy of crack topologies and noise in collected crack pictures. Pavement crack detection
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I. LITERATURE REVIEW Crack detection is identified and segmented based on a variety of characteristics because it is an intriguing issue with numerous challenging parameters. There are different ways to find cracks. There are several techniques that use manual inputs to find fractures in a variety of surfaces. Image processing methods using cameras. This section provides a summary of the processing methods used to identify cracks in engineering structures using camera images. Many of the publications that were reviewed here used camera input.
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