Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Abstract

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.

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Cited by in corpus (5)

• Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture
• Optimized Deep Encoder-Decoder Methods for Crack Segmentation
• Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
• CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback
• Deep Learning for Pavement Condition Evaluation Using Satellite Imagery