ARTIFICIAL INTELLIGENCE FOR REAL-TIME MONITORING OF LOGS ON THE MADEIRA RIVER: A CASE STUDY ON JIRAU HYDROELECTRIC PLANT

Abstract

The Jirau and Santo Antônio hydroelectric plants in Rondônia implemented a methodology using high-range cameras and artificial intelligence technology to address the challenge of managing logs transported by the river during floods. By applying machine learning techniques and neural networks, the system automatically monitors log transport and accumulation. Python 3, along with libraries like OpenCV, PIL, Numpy, and Pytorch, was utilized for efficient implementation. The methodology includes frame selection, log and debris segmentation, perspective correction, and log counting. Training was conducted using annotated images, and the detection process involved color segmentation, noise removal, and morphological operations. The calculated log and debris occupancy results were stored in a SQL database and presented on Power BI dashboards. The system aims to improve log management, ensuring power generation and ecological order are safeguarded.