Automated Visual Inspection of Bridge Defect Segmentation Using Large-Scale Pretrained Models
TL;DR
This paper presents a scalable automated visual inspection pipeline for semantic segmentation of bridge defects, targeting the 19-class dacl10k benchmark dataset, and performs the first systematic comparison of pretraining paradigms on dacl10k, including CNN -supervised, ViT-supervised, masked image modelling, and self-supervised learning across 10 configurations under a controlled training protocol.
Abstract
Routine visual inspections of bridges are safety-critical activities that are still manual, time-consuming, and subjective to the inspector’s interpretation. This paper presents a scalable automated visual inspection pipeline for semantic segmentation of bridge defects, targeting the 19-class dacl10k benchmark dataset. We perform the first systematic comparison of pretraining paradigms on dacl10k, including CNN -supervised, ViT-supervised, masked image modelling, and self-supervised learning across 10 configurations under a controlled training protocol. The pretraining paradigm consistently dominates architectural choice, with DINOv2-L, pretrained on 142 million unlabelled images, achieving a mean Intersection-over-Union (mIoU) of 49.16%. Applying our native multi-label training approach to EVA-02-L, the dacl10k challenge-winning backbone, achieves 48.97% mIoU versus their 47.80% single-model result, demonstrating that training design is an independent performance factor. A three-model ensemble achieves 51.08% mIoU, exceeding the challenge winner’s score by using half the number of models. A prototype inspection system (InSpectralytiX) is deployed in a HuggingFace Gradio Space, demonstrating end-to-end feasibility from raw image to a per-class defect map. The future work targets automated condition scoring for bridge asset management integration, supporting structural health monitoring at the local level when performed repeatedly.