发表机构
Kent State University; University of North Texas（肯特州立大学; 北德克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对施工图像数据集缺乏恶劣条件样本的问题，提出成对合成数据集ConSynth-X，含34,199张图像，覆盖多种恶劣条件，支持多任务并验证了其保真度，以提升视觉模型鲁棒性。
AI 中文摘要
用于施工监测的计算机视觉系统在不利的环境和视觉条件下可能会性能下降，然而这些条件在现有的施工图像数据集中代表性不足。我们提出了ConSynth-X，一个成对合成的施工现场图像数据集，包含从3,109个真实世界源场景中导出的34,199张图像。该数据集包含11个特定条件的子集，涵盖降水、雾、夜间照明、夜间恶劣天气以及小目标或远距离视图。每张合成图像都与其对应的源场景相关联，从而能够在不同环境和视觉条件下进行受控比较。ConSynth-X包含源自源的标注、生成元数据、来源信息和图像质量指标，支持目标检测、图像描述、视觉定位和视觉问答。技术验证使用基于嵌入的相似性和分布分析来评估源-合成保真度以及与真实恶劣条件图像的一致性。该数据集为评估和提高施工视觉及视觉语言模型在具有挑战性的现场条件下的稳健性提供了结构化资源。
英文摘要
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.
Comments21 pages, 7 figures, 7 tables. Dataset and code are publicly available
