Densely connected deep neural network considering connectivity of pixels for automatic crack detection

In order to develop smart cities, the demand for assessing the condition of existing infrastructure systems in an automated manner is burgeoning rapidly. Among all the early signs of potential damage in infrastructure systems, formation of cracks is a critical one because it is directly related to the structural capacity and could significantly affect the serviceability of the infrastructure. This paper proposes a novel deep learning-based method considering the connectivity of pixels for automatic pavement crack detection which has the potential to complement the current practice involving visual inspection which is costly, inefficient and time-consuming. In the proposed method, the convolutional layers are densely connected in a feed-forward fashion to reuse features from multiple layers, and transposed convolution layers are used for multiple level feature fusion. A novel loss function considering the connectivity of pixels is introduced to overcome the issues related to the output of transposed convolution layers. The proposed method is tested on two datasets, where the first one is collected from a handheld smartphone and the second one is collected from a high-speed camera mounted on the rear of a moving car. In both datasets, the proposed method shows superior performance than other available methods.