![]() The model performance was then compared against a state-of-the-art patchwise model, as well as traditional edge detection and adaptive thresholding alternatives, and its advantages were illustrated. Sensitivity analysis showed that CrackPix was capable of correctly detecting over 92% of crack pixels and 99.9% of noncrack pixels in the validation set. To develop and train these models, a concrete crack image data set was collected and carefully annotated at the pixel level and was then used to train the model. A transposed convolution layer is then used to upsample and resize the resulting prediction heatmap to the size of the input images, thus providing pixel-level predictions. The deep fully convolutional model for crack detection introduced in this paper (CrackPix) leverages well-known image classification architectures for dense predictions by transforming their fully connected layers into convolutional filters. Although coarse patch-level deep learning crack detection models abound in the literature and have shown promise, the coarse level of detail provided, together with the requirement for fixed-size input images, significantly detract from their applicability and usefulness for refined damage analysis. ![]() This paper introduces the idea of using deep fully convolutional neural networks for pixel-level defect detection in concrete infrastructure systems.
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