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Deep Feature Extractor with Information-Extreme Decision Rules for Visual Classification of Sewer Pipe Defects and its Training Method | IEEE Conference Publication | IEEE Xplore
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Deep Feature Extractor with Information-Extreme Decision Rules for Visual Classification of Sewer Pipe Defects and its Training Method


Abstract:

CCTV inspections are frequently used to diagnose defects in underground sewer pipes. A model for classification of sewer pipe defects in video frames and a corresponding ...Show More

Abstract:

CCTV inspections are frequently used to diagnose defects in underground sewer pipes. A model for classification of sewer pipe defects in video frames and a corresponding five-phase training method are proposed. The model is based on deep convolutional neural network with a sigmoid output layer and information-extreme decision rules. The training method includes augmentation, feature extractor training resemblant of a Siamese network with triplet mining and softmax, computation of binary code for each class, training with joint binary cross-entropy loss using binary codes for each class as label and optimization of a hyper-spherical container for each class in Hamming space based on information criterion. Information criterion, expressed in the paper as logarithmic function of accuracy characteristics, provides a robust and reliable model for the most difficult case in the statistical sense. Results of a simulation with the optimal model on a dataset provided by Ace Pipe Cleaning, Inc confirm the efficiency of the proposed model and method and their suitability for practical usage.
Date of Conference: 21-25 August 2020
Date Added to IEEE Xplore: 23 September 2020
ISBN Information:
Conference Location: Lviv, Ukraine

References

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