Visual web scraper crack6/9/2023 A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. A comparative study evaluates the successfulness of the detailed surface categorization. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures.
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