论文标题
建筑物检查工具包：统一评估和强大的基准损害识别
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition
论文作者
论文摘要
近年来，几家公司和研究人员已经开始解决建筑结构自动检查范围内的损害识别问题。尽管公司既不愿意发布相关的数据也不愿意，但研究人员一方面面临数据短缺问题，而数据集则不一致，而另一方面没有一致的指标。这导致了无与伦比的结果。因此，我们介绍了建筑物检查工具包 - 自行车 - 该工具包可以用作易于使用损害识别领域中包含相关开源数据集的数据中心。数据集充满了评估拆分和预定义的指标，适合特定任务及其数据分布。为了兼容并激励该领域的研究人员，我们还提供了一个排行榜，并有可能与社区分享模型权重。作为起点，我们为使用大量的超参数搜索的多目标分类任务提供了强大的基准，该任务使用三种转移学习方法用于最先进的算法。该工具包和排行榜可在线提供。
In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsistent dataset splitting with the absence of consistent metrics on the other hand. This leads to incomparable results. Therefore, we introduce the building inspection toolkit -- bikit -- which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community. As starting point we provide strong baselines for multi-target classification tasks utilizing extensive hyperparameter search using three transfer learning approaches for state-of-the-art algorithms. The toolkit and the leaderboard are available online.
