If the physical constraints extracted from the training data do not accurately reflect real-world physical relationships, the performance of PhyFea could be adversely affected. Inaccurate constraints may lead to the model enforcing incorrect relationships during the segmentation process, resulting in predictions that are not only semantically incorrect but also physically implausible.
For example, if the model incorrectly assumes that a sidewalk can be above a road due to flawed training data, it may produce segmentations that violate basic physical principles, leading to a decrease in mean Intersection over Union (mIoU) scores. Furthermore, the model's ability to generalize to unseen data could be compromised, as it may rely on these faulty constraints rather than learning robust features from the data itself.
To mitigate this risk, it is crucial to ensure that the training datasets are comprehensive and accurately annotated, capturing the essential physical relationships among classes. Regular empirical analyses and validation against real-world scenarios can help refine the constraints and improve the overall performance of PhyFea.