Deep Learning-Based Detection of Defects from Images
- Authors: Srimanta Pal1, Sumita Das2, Sayani Manna3
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View Affiliations Hide Affiliations1 Maulana Abul Kalam Azad University of Technology, NH 12 (Old NH-34) Simhat Haringhata, Nadia 741249, West Bengal, India 2 Maulana Abul Kalam Azad University of Technology, NH-12 (Old NH-34) Simhat Haringhata, Nadia 741249, West Bengal, India 3 Maulana Abul Kalam Azad University of Technology, NH-12 (Old NH-34) Simhat Haringhata, Nadia 741249, West Bengal, India
- Source: Green Industrial Applications of Artificial Intelligence and Internet of Things , pp 176-182
- Publication Date: July 2024
- Language: English
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Crack detection has vital importance for monitoring and inspection of buildings. It has great significance for structural safety. This is a challenging task for computer vision and machine learning, as cracks only have low-level features for detection. Convolutional Neural Networks (CNN) is a very promising framework for crack detection from images with high accuracy and precision. This paper is based on a deep-learning methodology to detect and recognize structural defects. The dataset is split into training and testing data which is used to train the model. Then this trained model is used to recognize and classify cracks in images. The dataset consists of concrete crack images. The data set used has two categories, images with cracks and without cracks. A Convolutional Neural Network model using Pytorch will be fit to predict the images if the images have any cracks or not. This paper compares the accuracy of various models.
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