Deep learning approach for detailed block pavement distress segmentation

초록

Block pavement significantly impacts user convenience, yet its evaluation and maintenance methods are inefficient and resource-intensive. This study applies deep learning models to detect distress in block pavement using a dataset of 10,298 images with five distress types: cracks, broken pavers, missing pavers, excessive joint width, and utility structures. The Hybrid TransUNet model outperformed all the compared models in multiclass and binary segmentation tasks. It effectively segmented broken pavers, missing pavers, excessive joint width, and utility structures but faced challenges with crack detection. Combining outputs from individual binary models for multiclass masking improved IoU accuracy by 5.97%, but this approach is resource-intensive and less practical. These findings highlight the potential of deep learning, especially the TransUNet hybrid model, for enhancing the accuracy and efficiency of automated block pavement distress detection tools.

키워드