Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 11
• Main Authors: Jia, Di, Zhu, Hong, Jia, Guohui, Song, Weidong, Gao, Lin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Alliances; Artificial neural networks; Asphalt pavements; Automation; Computer vision; Cracks; Damage detection; Deep learning; Flaw detection; Maintenance and repair; Modules; Neural networks; Noise; Nonuniformity; Pavements; Pixels; Semantics; Spatial discrimination; Spatial resolution; Topology; Transportation; China; New Jersey
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2020/6412562

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.