Name
Title | ||
|---|---|---|
Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning |
Abstract | ||
|---|---|---|
Video and image sources are frequently applied in the area of defect inspection in industrial community. For the recognition and classification of sewer defects, a significant number of videos and images of sewers are collected. These data are then checked by human and some traditional methods to recognize and classify the sewer defects, which is inefficient and error-prone. Previously developed features like SIFT are unable to comprehensively represent such defects. Therefore, feature representation is especially important for defect autoclassification. In this paper, we study the automatic extraction of feature representation for sewer defects via deep learning. Moreover, a complete automatic system for classifying sewer defects is proposed built on a two-level hierarchical deep convolutional neural network, which shows high performance with respect to classification accuracy. The proposed network is trained on a novel data set with over 40 000 sewer images. The system has been successfully applied in the practical production, confirming its robustness and feasibility to real-world applications. The source code and trained model are available at the project website.
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<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic>
—Automatic defect inspection has become a fundamental research topic in engineering application field. Specifically, sewer defect detection is an important measure for maintenance, renewal, and rehabilitation activities of sewer infrastructure. In the current operation procedure, all the captured videos need to be inspected by experts frame by frame to recognize defects, yielding a significant low inspection rate with a significant amount of time. Previous work has attempted to employ traditional image processing methods for automated sewer defect classification. However, these methods get poor generalization capabilities since they use pre-engineered features. In most cases, sewerage inspection companies have to hire numerous professional inspectors to do this job, thereby consuming a lot of human and material resources. To address this problem, the authors propose an automatic detection and classification method for sewer defects based on hierarchical deep learning. Demonstrated by various experiments, the designed framework achieves a high defect classification accuracy, which can be easily integrated into an automatic sewer defect inspection system.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tase.2019.2900170 | IEEE Transactions on Automation Science and Engineering |
Keywords | Field | DocType |
Inspection,Deep learning,Feature extraction,Task analysis,Labeling,Image segmentation,Convolutional neural networks | Data mining,Computer science,Convolutional neural network,Image processing,Feature extraction,Image segmentation,Control engineering,Robustness (computer science),Sewerage,Artificial intelligence,Deep learning,Sanitary sewer | Journal |
Volume | Issue | ISSN |
16 | 4 | 1545-5955 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Xie | 1 | 16 | 9.82 |
| Dawei Li | 2 | 4 | 1.41 |
| Jinxuan Xu | 3 | 3 | 0.38 |
| Zhenghao Yu | 4 | 4 | 1.47 |
| Jun Wang | 5 | 372 | 47.52 |