Identification of surface defects using deep and transfer learning

The purpose of this study was to employ a previously trained (pre-trained) convolutional neural network called Resnet101 in conjunction with deep machine learning approaches in order to construct an algorithm for classifying cracks in the photos that were evaluated. Adjustments were made to the ultimate layer, which resulted in the fully connected layer being altered. Specifically, the basic 1000-output fully connected layer in Resnet101 was replaced with a binary-classification layer, which consisted of two categories: an image with cracks and an image without cracks. In this study, we investigate whether or not it is possible to use deep neural networks to accomplish the rapid and entirely automated detection of flaws by utilizing analyzed photographs as the data source. The research that was done led to the discovery that a pre-trained convolutional neural network that makes use of support vector machines to train a fully connected layer is quite an efficient option, and that the acquired forecasting algorithm allows the categorization of faults with extremely good accuracy. The proposed classification algorithm is 99 percent efficient. In material inspection tasks, this idea can be used to find cracks and other flaws in the material, such as those that could be found in a number of public structures like buildings, roads, and bridges.