Indoor Surface Classification for Mobile Robots

In this project, we generated a dataset that contains three different types of indoor floor surfaces: carpet, tile and wood. Then, we used this dataset to train eight CNN-based models, including our proposed model, <em><strong>MobileNetV2-modified</strong></em>. The dataset comprises a total of 2081 samples, consisting of images captured with cameras in various indoor environments and lighting conditions. These images were taken from different angles in accordance with the overall dimensions of the indoor robots. This dataset includes samples collected from more than 20 different indoor environments. The dataset consists of 870 carpet samples, 638 tile samples and 573 wood surface samples.