论文标题
裂纹检测是一个弱监督的问题：实现较少的注释密集型裂纹探测器
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
论文作者
论文摘要
自动裂纹检测是一项关键任务，有可能大幅度减少目前正在手动进行劳动密集型建筑物和道路检查。该领域的最新研究显着提高了检测准确性。但是，这些方法通常在很大程度上依赖于昂贵的注释过程。此外，要处理各种目标域，通常需要每个新环境的新批次注释。这使得在现实生活中部署裂纹检测系统时，数据注释的成本是显着的瓶颈。为了解决此问题，我们将裂纹检测问题提出为一个弱监督的问题，并提出了一个两分支的框架。通过结合对低质量注释训练的监督模型的预测以及基于像素亮度的预测，我们的框架不受注释质量的影响。实验结果表明，即使提供低质量注释，提出的框架仍保留高检测精度。所提出的框架的实施可在https://github.com/hitachi-rd-cv/weakly-sup-crackdet上公开获得。
Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems in real life. To resolve this issue, we formulate the crack detection problem as a weakly-supervised problem and propose a two-branched framework. By combining predictions of a supervised model trained on low quality annotations with predictions based on pixel brightness, our framework is less affected by the annotation quality. Experimental results show that the proposed framework retains high detection accuracy even when provided with low quality annotations. Implementation of the proposed framework is publicly available at https://github.com/hitachi-rd-cv/weakly-sup-crackdet.
