Deep Learning Based Dirt Detection and Cleanliness Evaluation in Autonomous Indian Domestic Concrete Water Tank Cleaning Robot

ABSTRACT Water tanks are vital in supplying and storing water for both domestic and industrial needs. Improper maintenance of water tanks leads to water contamination. Contaminated water is one of the main contributors to skin and hair diseases. It is crucial to ensure that water tanks are kept clean and free of contaminants, especially in residential and commercial settings. Currently, there are hardly any method for dirt or stain detection and cleanliness evaluation after cleaning the tanks. In addition, manual methods are still widely used to clean these tanks, and the process is tiresome and time‐consuming. Autonomous water tank cleaning robots have been suggested to address these challenges, performing the cleaning process with minimal human intervention, thereby reducing time, effort, and risks. Although research has been conducted in this field, there has been limited progress in integrating artificial intelligence (AI)‐based cleanliness evaluation modes into robotic systems. This research proposes an autonomous robot capable of cleaning both the floor and walls of concrete water tank structures. The robot incorporates a custom‐trained modified U‐Net model to detect and clean residual dirt patches missed during the initial cleaning. The models were trained and validated on a custom dataset of 7300 images. Among all the trained segmentation models, the modified U‐Net model achieved a validation accuracy of 96%.