Convolutional neural network for automated classification of jointed plain concrete pavement conditions

The detailed monitoring of jointed plain concrete pavement (JPCP) slab condition is essential for cost‐effective JPCP maintenance and rehabilitation. However, existing visual inspection practices for detailed slab condition classification are time‐consuming and labor‐intensive. In this paper, we proposed an automated JPCP slab condition classification model based on convolutional neural networks (ConvNets), which is the first to perform multi‐label classification on the JPCP slab condition based on both crack types and severity levels. To handle the different scales between JPCP slab condition states, the model includes a novel global context block with atrous spatial pyramid pooling, denoted as a GC‐ASPP block. The block can be flexibly applied to any ConvNets to effectively model the global context of images with the extraction of multiscale image features. The proposed model was evaluated using real‐world 3D JPCP surface data. With the GC‐ASPP block, our best model achieved an average precision of 85.42% on multi‐label slab condition classification.