dacl10k

Reliably identifying reinforced concrete defects (RCDs) plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.

精准识别钢筋混凝土缺陷（Reinforced Concrete Defects, RCDs），对评估全球最常见的桥梁类型——混凝土桥梁的结构完整性、交通安全与长期耐久性至关重要。然而，当前用于钢筋混凝土缺陷识别的公开数据集在样本规模与类别多样性上均存在明显不足，这使其在真实场景中的实用性以及作为基准数据集的价值受到质疑。针对该问题，本文提出dacl10k数据集：这是一个具备极强类别多样性的多标签语义分割用钢筋混凝土缺陷数据集，其9920张图像均来源于真实桥梁巡检场景。dacl10k涵盖12类缺陷类别与6类桥梁构件，这些类别均为桥梁评估以及制定修复工程、交通荷载限制、桥梁封闭等处置方案的核心依据。此外，本文还针对dacl10k构建了基线模型并开展评测，其中最优模型在测试集上的平均交并比（mean Intersection over Union, mIoU）达到0.42。目前，dacl10k数据集及其配套基线模型将对所有科研人员与工程从业者开放，是当前桥梁巡检领域语义分割任务中，在样本量与类别多样性方面规模最大的公开数据集。

搜集汇总

背景与挑战

背景概述

dacl10k是一个包含9,920张桥梁检查图像的多标签语义分割数据集，用于识别12种损伤类别和6种桥梁组件，是目前桥梁检测领域最大且最具多样性的数据集。