synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections?

TL;DR

This paper introduces synth-dacl, synthetic data extensions for a bridge inspection dataset, significantly improving segmentation accuracy and robustness of models, especially for fine-grained defect classes like cracks and cavities.

Contribution

The work presents three novel synthetic dataset extensions that balance class distribution and enhance model performance in real-world bridge defect segmentation.

Findings

Models trained with synth-dacl show a 2% increase in mean IoU and F1 score on perturbed test sets.

Synthetic data improves robustness against variations in image quality and background textures.

Enhanced segmentation accuracy for fine-grained classes like cracks and cavities.

Abstract

Adequate bridge inspection is increasingly challenging in many countries due to growing ailing stocks, compounded with a lack of staff and financial resources. Automating the key task of visual bridge inspection, classification of defects and building components on pixel level, improves efficiency, increases accuracy and enhances safety in the inspection process and resulting building assessment. Models overtaking this task must cope with an assortment of real-world conditions. They must be robust to variations in image quality, as well as background texture, as defects often appear on surfaces of diverse texture and degree of weathering. dacl10k is the largest and most diverse dataset for real-world concrete bridge inspections. However, the dataset exhibits class imbalance, which leads to notably poor model performance particularly when segmenting fine-grained classes such as cracks and cavities. This work introduces "synth-dacl", a compilation of three novel dataset extensions based on synthetic concrete textures. These extensions are designed to balance class distribution in dacl10k and enhance model performance, especially for crack and cavity segmentation. When incorporating the synth-dacl extensions, we observe substantial improvements in model robustness across 15 perturbed test sets. Notably, on the perturbed test set, a model trained on dacl10k combined with all synthetic extensions achieves a 2% increase in mean IoU, F1 score, Recall, and Precision compared to the same model trained solely on dacl10k.