SEWER-SEER: Robotic Computer Vision System for the Diagnosis of Sewer Pipes using Artificial Intelligence for the Department of Drinking Water and Sanitation of the Intercultural GAD of the Cañar Canton

The Sewer-Seer project addresses the challenges of sewer management in the GAD of Cañar, where aging infrastructure and manual inspection methods create risks for workers and lead to inefficient diagnostics. The proposed solution consists of a mobile robotic system equipped with computer vision and artificial intelligence, capable of identifying blockages, cracks, and structural damage in real-time. The methodological approach includes creating a database of pipe images, training deep learning models (ResNet), and integrating specialized hardware. This technological development allows for accurate diagnostics without the need for personnel to enter confined spaces, significantly reducing exposure to toxic gases and contaminated water. As a result, the project seeks to optimize maintenance times, reduce operational costs, and minimize unnecessary excavations. Furthermore, a technology transfer phase is included, which involves technical training for municipal staff and the dissemination of results within the scientific community, promoting a sustainable and technologically advanced management model for local water infrastructure.<br/><br/><b>Goal</b>: <br/>Develop a robotic computer vision system based on artificial intelligence for the accurate diagnosis of sewer pipes in the Cañar canton. The project aims to improve operational efficiency and occupational safety by automating the inspection of underground networks.<br/><br/><b>Research lines</b>: <br/>Condition Monitoring