| Resumo: | The implementation of a robust vision system in floor-cleaning robots enables them to optimize their navigation and analysing the surrounding floor, leading to a reduction on power, water and chemical products’ consumption. In this paper, we propose a novel pipeline of a vision system to be integrated into floor-cleaning robots. This vision system was built upon the YOLOv5 framework, and its role is to detect dirty spots on the floor. The vision system is fed by two cameras: one on the front and the other on the back of the floor-cleaning robot. The goal of the front camera is to save energy and resources of the floor-cleaning robot, controlling its speed and how much water and detergent is spent according to the detected dirt. The goal of the back camera is to act as evaluation and aid the navigation node, since it helps the floor-cleaning robot to understand if the cleaning was effective and if it needs to go back later for a second sweep. A self-calibration algorithm was implemented on both cameras to stabilize image intensity and improve the robustness of the vision system. A YOLOv5 model was trained with carefully prepared training data. A new dataset was obtained in an automotive factory using the floor-cleaning robot. A hybrid training dataset was used, consisting on the Automation and Control Institute dataset (ACIN), the automotive factory dataset, and a synthetic dataset. Data augmentation was applied to increase the dataset and to balance the classes. Finally, our vision system attained a mean average precision (mAP) of 0.7 on the testing set.
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