Semi-supervised semantic segmentation using cross-consistency training for pavement crack detection

Abstract

Crack detection is crucial to providing information for assessing pavement condition and maintaining the safety of infrastructure. Traditional manual crack detection methods are relatively time-consuming and thus have gradually been replaced by automatic detection in recent years. However, most semantic segmentation algorithms require the manual labelling of a large amount of data, which consumes a great deal of time. A semi-supervised semantic word segmentation method based on cross-consistency training for road crack detection is established in this paper. To take advantage of unlabelled crack samples, this study enforced consistency between the primary and secondary decoder predictions, using different disturbed versions of the encoder output as inputs to improve the encoder representation. When only 60% of the annotated data were used, the applied method achieved a better performance than other mainstream semantic segmentation algorithms. Therefore, the applied method can be used in automated and efficient pavement detection for pavement inspection and maintenance.

KEYWORDS:

• Semantic segmentation
• deep learning
• semi-supervised network
• crack detection

Disclosure statement

No potential conflict of interest was reported by the authors().

Additional information

Funding

This work was supported by National Natural Science Foundation of China [51878193]; Graduate Basic Innovation Project of Guangzhou University [2021GDJC-D10].