Journal
IEEE ACCESS
Volume 7, Issue -, Pages 171001-171012Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2956191
Keywords
Pavement crack detection; crack classification; convolutional neural network; multiscale feature extraction; attention mechanism
Categories
Funding
- Public Welfare Research Fund in Liaoning Province, China [20170003]
- Key Natural Science Plan Fund of Liaoning Province, China [20170520141]
- Department of Education of Liaoning Province, China [LR2016045]
- National Natural Science Foundation of China [41871379, 61601213]
Ask authors/readers for more resources
Pavement crack detection and characterization is a fundamental part of road intelligent maintenance systems. Due to the high non-uniformity of cracks, topological complexity, and similar noise from crack texture, the challenge arises in this domain with automated crack detection and classification in a complex environment. In this work, an overarching framework for a universal and robust automatic method that simultaneously characterizes the type of crack and its severity level was developed. For crack detection, we propose a novel and efficient crack detection network that captures the crack context information by establishing a multiscale dilated convolution module. On this foundation, an attention mechanism is introduced to further refine the high-level features. Moreover, the rich features at different levels are fused in an upsampling module to generate more detailed crack detection results. For crack classification, a novel characterization algorithm is developed to classify the type of crack after detection. The crack segment branches are then merged and classified into four types: transversal, longitudinal, block, and alligator; the severity levels of cracks are assessed by calculating the average width and distance between the crack branches. The proposed crack detection method effectively detects crack information in a complex environment, and achieves the current state-of-the-art accuracy. Compared to manual classification results, the classification accuracy of transversal and longitudinal cracks is higher than 95%, and the classification accuracy of block and alligator is above 86%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available