Use of Parallel ResNet for High-Performance Pavement Crack Detection and Measurement

Zhun Fan, Huibiao Lin, Chong Li, Jian Su, Salvatore Bruno and Giuseppe Loprencipe
Additional contact information
Zhun Fan: Key Lab of Digital Signal and Image Processing of Guangdong Province, Shantou 515063, China
Huibiao Lin: Key Lab of Digital Signal and Image Processing of Guangdong Province, Shantou 515063, China
Chong Li: Key Lab of Digital Signal and Image Processing of Guangdong Province, Shantou 515063, China
Jian Su: Guangzhou Environmental Protection Investment Nansha Environmental Energy Co., Ltd., Guangzhou 511470, China
Salvatore Bruno: Department of Civil, Construction and Environmental Engineering, Sapienza University, Via Eudossiana, 18-00184 Rome, Italy
Giuseppe Loprencipe: Department of Civil, Construction and Environmental Engineering, Sapienza University, Via Eudossiana, 18-00184 Rome, Italy

Sustainability, 2022, vol. 14, issue 3, 1-21

Abstract: In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy.

Keywords: pavement monitoring; crack detection; crack measurement; automated distress evaluation systems; image processing; convolutional neural networks; residual network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/3/1825/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/3/1825/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:3:p:1825-:d:742705

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().