Comprehensive image datasets can benefit the construction industry in terms of serving as the basis for generating deep-learning-based object detection models and testing the performance of object detection algorithms, but building such datasets is complex and requires vast professional knowledge. This paper develops and publicly releases a new large-scale image dataset specifically collected and annotated for the construction site, called Site Object Detection Dataset (SODA), which contains 15 object classes categorized by the worker, material, machine, and layout. >20,000 images were collected from multiple construction sites in different situations, weather conditions, and construction phases, covering different angles and perspectives. Statistical analysis shows that the dataset is well developed in terms of diversity and volume. Further evaluation with two widely-adopted deep learning-based object detection algorithms also illustrates the feasibility of the dataset, achieving a maximum mAP of 81.47%. This research contributes a large-scale open image dataset for the construction industry and sets up a performance benchmark for further evaluation of relevant algorithms.

综合图像数据集可以作为生成基于深度学习的对象检测模型和测试对象检测算法性能的基础，使建筑行业受益，但构建此类数据集很复杂，需要大量的专业知识。本文开发并公开发布了一个专门为施工现场收集和注释的新的大规模图像数据集，称为站点对象检测数据集（SODA），其中包含按工人、材料、机器和布局分类的 15 个对象类。在不同情况、天气条件和施工阶段，从多个施工现场收集了超过 20,000 张图像，涵盖了不同的角度和视角。统计分析表明，该数据集在多样性和数量方面发展良好。对两种广泛采用的基于深度学习的目标检测算法的进一步评估也说明了数据集的可行性，最大 mAP 为 81.47%。该研究为建筑行业提供了一个大规模的开放图像数据集，并为进一步评估相关算法建立了性能基准。