Application of deep neural networks in classifying images of sewer network damage and identifying their critical paths

Abstract

Sewage flow path is the main component of urban sewerage network infrastructure. Damage to sewers is less noticeable due to invisibility, and this failure to handle the damage leads to emergencies and unreasonable costs. These vital arteries need to be maintained and rebuilt during service for optimal performance in all dimensions. Nowadays, the methods of processing and classifying photos and videos taken by mobile videometer robots are widely used to inspect the sewer network. One of the successful algorithms in the field of image processing is the convolutional neural network algorithm, which is a subset of deep learning algorithm. In this paper, a convolutional neural network algorithm is used to classify images of sewer network damage and cases affecting the improvement, accuracy and performance of this algorithm. The images were obtained by a videometric robot from the sewer network. Results of using the proposed algorithm in the sewerage network, achieving 98% accuracy in classifying network faults and compared to other methods and also reducing the relatively low execution time of the proposed architecture (91 minutes) compared to other architectures valid ones are the same in deep learning on the same hardware platform. Also, in the future, the proposed algorithm will be used to analyze networks without the need for specialized personnel and also to control an automatic network videometry robot.

Keywords

• deep learning algorithm deep learning algorithm
• convolution neural network
• sewer‌ network videometry
• image processing