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

2026-09-21

AI Summary

为解决建筑监测中计算机视觉系统在恶劣条件下性能下降的问题，通过创建包含34,199张合成图像的ConSynth-X数据集，涵盖多种不利条件，以增强模型鲁棒性。

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.

Problem

• construction monitoring
• adverse conditions
• computer vision
• image dataset
• robustness

Innovation

• Paired Synthetic Dataset
• Adverse Conditions
• Construction Monitoring
• Visual Robustness
• ConSynth-X