A paired synthetic construction-site image dataset for robust computer vision under adverse conditions

Viet Huy Duong; Ruoxin Xiong; Md Abdullah Al Forhad; Weishi Shi

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arXiv · 2609.24075

A paired synthetic construction-site image dataset for robust computer vision under adverse conditions

Viet Huy Duong·Ruoxin Xiong·Md Abdullah Al Forhad·Weishi Shi

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Abstract

Computer-vision systems used for construction monitoring can degrade under adverse environmental and visual conditions, yet such conditions remain underrepresented in existing construction image datasets. We present ConSynth-X, a paired synthetic construction-site image dataset containing 34,199 images derived from 3,109 real-world source scenes. The dataset comprises 11 condition-specific subsets spanning precipitation, fog, nighttime illumination, adverse weather at night, and small-object or long-distance views. Each synthetic image is linked to its corresponding source scene, enabling controlled comparison across environmental and visual conditions. ConSynth-X includes source-derived annotations, generation metadata, provenance information, and image-quality indicators, supporting object detection, image captioning, visual grounding, and visual question answering. Technical validation evaluates source-synthetic fidelity and alignment with real adverse-condition imagery using embedding-based similarity and distributional analyses. The dataset provides a structured resource for evaluating and improving the robustness of construction vision and vision-language models under challenging field conditions.

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Viet Huy Duong, Ruoxin Xiong, Md Abdullah Al Forhad, Weishi Shi. 2026-09-21. A paired synthetic construction-site image dataset for robust computer vision under adverse conditions. https://arxiv.org/abs/2609.24075

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