Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

Highlights

An image-recognition-based diagnosis system of pipe defect types was established.

1043 practical pipe images were gathered by CCTV robot in a southern Chinese city.

The overall accuracy of the system is 84% and the highest accuracy is 99.3%.

The accuracy shows positive correlation to the number of training samples.

Abstract

Closed circuit television (CCTV) systems are widely used to inspect sewer pipe conditions. During the diagnosis process, the manual diagnosis of defects is time consuming, labor intensive and error prone. To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images, this paper presents an image recognition algorithm that applies features extraction and machine learning approaches. An algorithm of image recognition techniques, including Hu invariant moment, texture features, lateral Fourier transform and Daubechies (DBn) wavelet transform, was used to describe the features of defects, and support vector machines were used to classify sewer pipe defects. According to the inspection results, seven defects were defined; the diagnostic system was applied to a sewer pipe system in a southern city of China, and 28,760 m of sewer pipes were inspected. The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%. The overall accuracy reached 84.1%. The diagnosing accuracy depended on the number of the training samples, and four fitting curves were applied to fit the data. According to this paper, the logarithmic fitting curve presents the highest coefficient of determination of 0.882, and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.

Graphical abstract

Keywords

Sewer pipe defects / Defect diagnosing / Image recognition / Multi-features extraction / Support vector machine

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Acknowledgements

This work was supported by the Mega-Projects of Science Research for Water Environment Improvement (Nos. 2011ZX07301-002 and 2017ZX07103-007). We would like to express our gratitude to the people from the local sewer management authority who provided the necessary help and convenience during the cooperation.

RIGHTS & PERMISSIONS

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