查看文章

This paper introduces the idea of using deep fully convolutional neural networks for pixel-level defect detection in concrete infrastructure systems. 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. 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. 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. To develop and train these models, a concrete crack image data set was …