4.6 Article

Pavement Surface Defect Detection Using Mask Region-Based Convolutional Neural Networks and Transfer Learning

Journal

APPLIED SCIENCES-BASEL
Volume 12, Issue 15, Pages -

Publisher

MDPI
DOI: 10.3390/app12157364

Keywords

defect detection; Mask R-CNN; Faster R-CNN; transfer learning; defect quantification

Funding

  1. Project of Guangdong Province High Level University Construction for Guangdong University of Technology [262519003]

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This study utilizes Mask R-CNN and transfer learning to detect pavement defects in complex backgrounds. The results show that Mask R-CNN performs better in terms of average precision, and models using FPN have shorter testing time. Additionally, the segmentation performance at different learning rates is analyzed, with Mask R-CNN using ResNet101 plus FPN achieving the best results.
Pavement defect detection is critical for pavement maintenance and management. Meanwhile, the accurate and timely detection of pavement defects in complex backgrounds is a huge challenge for maintenance work. Therefore, this paper used a mask region-based convolutional neural network (Mask R-CNN) and transfer learning to detect pavement defects in complex backgrounds. Twelve hundred pavement images were collected, and a dataset containing corresponding instance labels of the defects was established. Based on this dataset, the performance of the Mask R-CNN was compared with faster region-based convolutional neural networks (Faster R-CNNs) under the transfer of six well-known backbone networks. The results confirmed that the classification accuracy of the two algorithms (Mask R-CNN and Faster R-CNN) was consistent and reached 100%; however, the average precision (AP) of the Mask R-CNN was higher than that of Faster R-CNNs. Meanwhile, the testing time of the models using a feature pyramid network (FPN) was lower than that of other models, which reached 0.21 s per frame (SPF). On this basis, the segmentation performance of the Mask R-CNN was further analyzed at three learning rates (LRs). The Mask R-CNN performed best with ResNet101 plus FPN as its backbone structure, and its AP reached 92.1%. The error rate of defect quantification was between 4% and 16%. It has an ideal detection effect on multi-object and multi-class defects on pavement surfaces, and the quantitative results of the defects can provide a reference for pavement maintenance personnel.

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