Obstruction level detection of sewers videos using convolutional neural networks

Worldwide, sewer networks are designed to transport wastewater to a centralized treatment plant to be treated and returned to the environment. This is a critical process for preventing waterborne illnesses, providing safe drinking water and enhancing general sanitation in society. To keep a perfectly operational sewer network several inspections are manually performed by a Closed-Circuit Television system to report the obstruction level which may trigger a cleaning operative. In this work, we design a methodology to train a Convolutional Neural Network (CNN) for identifying the level of obstruction in pipes. We gathered a database of videos to generate useful frames to fed into the model. Our resulting classifier obtains deployment ready performances. To validate the consistency of the approach and its industrial applicability, we integrate the Layer-wise Relevance Propagation (LPR) algorithm, which endows a further understanding of the neural network behavior. The proposed system provides higher speed, accuracy, and consistency in the sewer process examination.This work is partially supported by the Consejo Nacional de Ciencia y Tecnologia (CONACYT), Estudiante No. CVU: 630716, by the RIS3CAT Utilities 4.0 SENIX project (COMRDI16-1-0055), cofounded by the European Regional Development Fund (FEDER) under the FEDER Catalonia Operative Programme 2014- 2020. It is also partially supported by the Spanish Government through Programa Severo Ochoa (SEV2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414).Peer ReviewedPostprint (published version

Sistema de visão computacional aplicado à inspeção automática interna de tubos de pequeno diâmetro

Orientador: Alessandro ZimmerCoorientador: Alceu Britto JúniorDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 26/07/2018Inclui referências: p. 56-58Resumo: Esta dissertação apresenta os principais aspectos de desenvolvimento de um sistema de aquisição e processamento de imagens que pode ser inserido em sistemas de tubos metálicos de termoelétricas a fim de se capturar imagens da região interna de tais tubos para análise. Após a imagem ser capturada, esta é processada resultando vários segmentos, nos quais são aplicados uma análise de textura e então utilizado um classificador para identificar de maneira automática alguns tipos de corrosão ou defeito. Os testes experimentais feitos com base em um conjunto de 2,615 imagens mostraram que os modelos propostos de classificação podem atingir taxas de acerto entre 90% e 94% ao classificarem um conjunto de images para teste, obtidas com um modelo de câmera, factível as dimensões demandadas do projeto, e 100% nas imagens de teste, obtidas com outro modelo de câmera, com dimensões não factíveis ao projeto final. Palavras-chave: Inspeção visual. Textura. Fusão de características. Inspeção automática.Abstract: This dissertation presents the main aspects of the design of an image acquisition and processing approach that can be inserted into thermoelectric metal pipe systems and travel inside the pipes to capture images from the inner surface of such pipes for further analysis. After the image capture, a texture analysis of its internal surface is carried out to classify automatically segments from the image that present some type of corrosion or defects. The experimental results on a dataset of 2,615 images have shown that proposed classification models can achieve accuracy between 90% and 94% on the test set, using a feasible camera for the project and 100% using another camera model. Keywords: Visual inspection. Texture. Fusion of features. Automatic inspection