Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN

The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (3), p.1215
Main Authors:	Xu, Xiangyang, Zhao, Mian, Shi, Peixin, Ren, Ruiqi, He, Xuhui, Wei, Xiaojun, Yang, Hao
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial neural networks Asphalt pavements Classification Comparative analysis Computer peripherals Computer vision crack detection Cracks Data mining Datasets Deep learning Faster R-CNN Flaw detection Innovations intelligent monitoring Machine learning Machine vision Mask R-CNN Methods Neural networks Neural Networks, Computer Recognition Training Visualization
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Summary:	The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.
ISSN:	1424-8220 1424-8220