Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 10
Abstract
The water supply pipe system is an important component of the municipal pipe system. However, water supply pipes usually suffer from various defects, such as deposits and infiltrations, which severely affect their performance and result in millions of dollars being wasted on maintenance work. Therefore, timely and effective inspection of water supply pipes is very important. In recent years, automatic detection based on deep learning methods has had the advantages of high efficiency, low cost, and time saving, thus gradually replacing manual inspection for defects in the pipe system. To solve the problem of unclear image acquisition for water supply pipes, this paper proposes a novel automated detection method for water supply pipe defects, mainly involving the use of underwater image enhancement (UIE) algorithms to improve data set image quality, and an attention mechanism was utilized to improve the You Only Look Once X (YOLOX) model for defects detection. Experimental results demonstrate that the improved YOLOX model based on the data set enhanced by underwater image enhancement and attention mechanism achieved an average accuracy [mean average precision (mAP)] value of 92.4% and F1 score of 0.86, which are better than traditional models. Finally, an efficient and accurate automated detection procedure for water supply pipe defects was provided.
Practical Applications
The automatic detection method of water supply pipe defects constructed in this research has the following three significant practical advantages: (1) the UIE algorithm is applied to the water supply pipe image, which improves the quality and quantity of the data set; (2) the method combines the improved UIE data set and attention mechanism to promote the efficiency and accuracy of the object detection model; (3) the research provides a novel procedure for the automatic detection work of water supply pipe defects. New attempts have been made in three aspects—the use of underwater pipeline robots for detection, the establishment of a high-quality data set, and the training or prediction of the object detection model for water supply pipe—and good results have been achieved. For these reasons, this method can greatly reduce the workload of construction personnel, and effectively avoid the occurrence of detection error events caused by the misjudgment of technicians.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the Innovation Group Science Foundation of the Natural Science Foundation of Chongqing, China (Grant No. cstc 2020jcyj-cxttX0003), the Key Program of the National Natural Science Foundation of China (52130901), and the Taishan Industry Leading Talents (tscx202306104). The authors express gratitude for their support.
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© 2024 American Society of Civil Engineers.
History
Received: Dec 20, 2023
Accepted: Apr 12, 2024
Published online: Jul 31, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 31, 2024
ASCE Technical Topics:
- Automation and robotics
- Defects and imperfections
- Engineering fundamentals
- Infrastructure
- Materials characterization
- Materials engineering
- Municipal water
- Pipeline systems
- Pipelines
- Pipes
- Systems engineering
- Underwater pipelines
- Water (by type)
- Water and water resources
- Water management
- Water pipelines
- Water supply
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