The excessive accumulation of foam in wastewater treatment plant (WWTP) tanks can impede proper aeration, hindering the effective removal of organic matter from the water. This study proposes a novel technique to monitor in real time the presence of foams in WWTP tanks by using texture segmentation models trained with centralized and federated approaches. These models are designed to segment the foam and quantify the percentage of foam coverage across the entire tank surface. This data provides plant operators with crucial information for identifying the optimal time for foam removal. The proposed methodology is integrated into an image processing pipeline that involves acquiring images using a PTZ camera, ensuring the absence of anomalies in the captured images, and implementing a real-time communication method for event notifications to plant operators. The models exhibit noteworthy performance, achieving an 86% Dice score in foam segmentation, with comparable results obtained through both centralized and federated training. Implemented in a wastewater treatment plant, this integrated pipeline enhances operational efficiency while concurrently reducing costs.
