Condition assessment and health monitoring (CAHM) of built assets requires effective and continuous monitoring of any changes to the material and/or geometric properties of the assets in order to detect any early signs of defects or damage and act on time. Most of the traditional CAHM techniques, however, depend on manual labour despite that, in some cases, the inspection environment can be unsafe and could lead to low efficiency or misjudgement of the severity of the defect. In recent years, computer vision techniques have been proposed as an automated alternative to the traditional CAHM techniques as methods for extracting and analysing feature-related information from asset images and videos. Such methods have proven to be robust and effective solutions, complementary to current time-consuming and unreliable manual observational practices. This work is concerned with the development of a deep learning-based smartphone app, which allows real-time detection of four types of defects in buildings, namely, cracks, mould, stain and paint deterioration. Since smartphones are widely available and equipped with high-resolution cameras, this application can offer a practical, low-cost solution for condition assessment procedures of built assets. The obtained results are promising and support the feasibility and effectiveness of the approach to identify and classify various types of building defects.

中文翻译：

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用于实时检测建筑物缺陷的深度学习智能手机应用程序

已建成资产的状态评估和健康监测 (CAHM) 需要对资产的材料和/或几何特性的任何变化进行有效和持续的监测，以便发现缺陷或损坏的任何早期迹象并及时采取行动。然而，大多数传统的 CAHM 技术依赖于手工劳动，尽管在某些情况下，检查环境可能不安全，并可能导致效率低下或对缺陷严重程度的错误判断。近年来，已提出计算机视觉技术作为传统 CAHM 技术的自动化替代方案，作为从资产图像​​和视频中提取和分析与特征相关的信息的方法。事实证明，此类方法是稳健有效的解决方案，可以补充当前耗时且不可靠的手动观察实践。这项工作涉及开发基于深度学习的智能手机应用程序，该应用程序可以实时检测建筑物中的四种缺陷，即裂缝、霉菌、污渍和油漆变质。由于智能手机随处可见并配备了高分辨率摄像头，因此该应用程序可以为建筑资产的状况评估程序提供实用、低成本的解决方案。所获得的结果是有希望的，并支持该方法识别和分类各种类型的建筑缺陷的可行性和有效性。此应用程序可为已建资产的状况评估程序提供实用、低成本的解决方案。所获得的结果是有希望的，并支持该方法识别和分类各种类型的建筑缺陷的可行性和有效性。此应用程序可为已建资产的状况评估程序提供实用、低成本的解决方案。获得的结果是有希望的，并支持该方法识别和分类各种类型的建筑缺陷的可行性和有效性。