Segmentation-Based Approach Towards Detecting Improperly Installed Tactile Paving.

Improperly installed tactile paving may cause confusion or accidents for visually impaired pedestrians. This preliminary study investigates an automated approach to identifying such installation errors. We adopt DeepLabV3+, a semantic segmentation model, to distinguish between two types of tactile blocks-warning and guiding blocks-thereby facilitating visual assessment of improper layouts by human observers. The model achieved a mean Intersection over Union (mIoU) of 0.937 +/- 0.005 across 5 cross-validation folds, demonstrating promising segmentation performance. We further discuss the effectiveness of this segmentation-based approach in enhancing the detectability of improper installations.