The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. This paper introduces a new semantic segmentation dataset specifically tailored for construction sites, taking into account the diverse challenges posed by adverse weather and environmental conditions. The dataset is designed to enhance the training and evaluation of object detection models, fostering their adaptability and reliability in real-world construction applications. Our dataset comprises annotated images captured under a wide range of different weather conditions, including but not limited to sunny days, rainy periods, foggy atmospheres, and low-light situations. Additionally, environmental factors such as the existence of dirt/mud on the camera lens are integrated into the dataset through actual captures and synthetic generation to simulate the complex conditions prevalent in construction sites. We also generate synthetic images of the annotations including precise semantic segmentation masks for various objects commonly found in construction environments, such as wheel loader machines, personnel, cars, and structural elements. To demonstrate the dataset's utility, we evaluate state-of-the-art object detection algorithms on our proposed benchmark. The results highlight the dataset's success in adversarial training models across diverse conditions, showcasing its efficacy compared to existing datasets that lack such environmental variability.

翻译：随着建筑环境中自主机器需求的日益增长，亟需开发能够在多种天气与环境条件下有效运行的鲁棒目标检测算法。本文提出一个专为建筑工地设计的新型语义分割数据集，充分考虑了恶劣天气与环境条件带来的多样化挑战。该数据集旨在增强目标检测模型的训练与评估，提升其在真实建筑应用中的适应性与可靠性。我们的数据集包含在广泛天气条件下采集的标注图像，包括但不限于晴天、雨天、雾天及低光照环境。此外，通过实际采集与合成生成，将泥土/泥浆附着于相机镜头等环境因素融入数据集，以模拟建筑工地常见的复杂条件。我们还生成了建筑环境中常见物体（如轮式装载机、人员、车辆及结构构件）的合成图像标注，包括精确的语义分割掩码。为展示数据集的实用性，我们在所提出的基准上评估了当前最先进的目标检测算法。结果表明，该数据集在多种条件下进行对抗训练模型方面成效显著，与现有缺乏此类环境变异性数据集相比，展现了其优越性。