Reliable Deep Learning-Based Analysis of Production Areas Using RGB-D Data and Model Confidence Calibration

In order to quickly adapt the factory layout to changed product variants or quantities, fast re-planning cycles are crucial for manufacturing companies. A promising approach to speed-up such processes is the acquisition of 3D scans of the factory's shopfloor. These can be used to correctly assess its current state and generate an up-to-date database for layout planning. However, the manual analysis of these 3D scans still constitutes a time-consuming task and the terrestrial LiDAR sensors commonly used for data acquisition are associated with high investment costs. We therefore present an approach for automated analysis of factory layouts based on data captured by a low-cost RGB-D sensor. Semantic segmentation is performed using the acquired color and depth images to classify the different visible areas automatically. On the one hand, the potential of multi- and uni-modal deep learning models is assessed. On the other hand, the use of model confidence calibration approaches is evaluated to improve the reliability of the predicted segmentation masks, avoid false predictions, and hence increase users' trust in the results.