Gradient and self-attention enabled convolutional neural network for crack detection in smart cities

Intelligent transportation is an important guarantee for the safety and efficiency of urban transportation in smart cities, and regular road pavement inspection is the focus of road and bridge maintenance in intelligent transportation. Cracks in concrete pavement are the most common type of pavement damage and the earliest sign of pavement deterioration. However, existing crack detection algorithms suffer from incomplete crack detection and are easily disturbed by pseudo-cracks such as water spots and leaves. To address the above problems, this paper proposes a convolutional neural network (CNN) method that introduces a gradient module and an attention mechanism. The method adopts a CNN model based on the VGG-16 structure as the main body of the network structure, and optimally adjusts the network structure by incorporating a gradient layer and a self-attention mechanism, accelerating the convergence speed of network training and the global information learning ability. A negative sample dataset with pseudo-cracks, such as leaves, water spots and branches was constructed, and comparative experimental analysis was conducted in terms of both visual judgment and objective indicators. The experimental results show that after the introduction of the gradient layer and the self-attentive mechanism, not only the convergence speed of the network training is faster, but also the cracks in the concrete pavement images can be segmented more completely and accurately.