  Automated pavement crack detection and measurement are important road issues.
Agencies have to guarantee the improvement of road safety. Conventional crack
detection and measurement algorithms can be extremely time-consuming and low
efficiency. Therefore, recently, innovative algorithms have received increased
attention from researchers. In this paper, we propose an ensemble of
convolutional neural networks (without a pooling layer) based on probability
fusion for automated pavement crack detection and measurement. Specifically, an
ensemble of convolutional neural networks was employed to identify the
structure of small cracks with raw images. Secondly, outputs of the individual
convolutional neural network model for the ensemble were averaged to produce
the final crack probability value of each pixel, which can obtain a predicted
probability map. Finally, the predicted morphological features of the cracks
were measured by using the skeleton extraction algorithm. To validate the
proposed method, some experiments were performed on two public crack databases
(CFD and AigleRN) and the results of the different state-of-the-art methods
were compared. The experimental results show that the proposed method
outperforms the other methods. For crack measurement, the crack length and
width can be measure based on different crack types (complex, common, thin, and
intersecting cracks.). The results show that the proposed algorithm can be
effectively applied for crack measurement.
