Unmanned aerial vehicle (UAV) for roofing inspection - Quality and scale assessment

建筑业的屋顶检查实践往往受到安全、可达性和工地的限制。 条件。屋顶通常需要使用热像仪(红外线)技术进行目视检查或扫描 通过屋顶、外墙或湿式绝缘材料的缝隙检测能量泄漏。然而，执行这样的手动操作 检查是不安全的，而且还受到天气条件和可达性的限制。因此，一次检查 SkyBEAM服务是由Tremco提出的，它使用配备有 高分辨率热像仪和摄像机，帮助定位设施可能泄漏能量的位置或可能 有潮湿的绝缘材料。尽管SkyBEAM在更大的安全性和访问性方面的优势是显而易见的 好处，无人机与人工检查方法相比产生的结果的质量需要 调查过了。无人机检测到的缺陷的准确性或范围尚不清楚，需要进一步 检验和现场数据分析。这项研究旨在生成一个健壮的数据分析框架来评估 SkyBEAM在节省时间、检查质量和检查规模方面的价值。为此， 研究目前工业中的替代检测方法，包括目视检测和人工检测 将对四栋建筑物进行热像扫描。由无人机拍摄的图像驱动的数据 然后与其他两种方法拍摄的图像的组合进行比较。由生成的报告 每种方法还将从位置和规模的准确性方面进行分析和比较 检测到的缺陷以及为每个缺陷提供的详细信息。然后对数据进行解释和分析，并 针对目前无人机在屋面检测中的应用现状，提出了改进建议。作为一名 结果，拟议的研究通过提供详细的改进措施来提高建筑物检验的安全性。 对当前技术的检查和报告。使用中提供的改进的检查系统 这项研究将极大地提高检查或状态评估的生产率，并减少 与之相关的负面风险。

Roof inspection practices in the construction industry are often limited by safety, accessibility, and site conditions. Roofs typically need to be visually inspected or scanned using thermographic (infrared) technology to detect leaking energy through gaps in the roofs, façade or wet insulation. However, performing such manual inspection is not safe and also is limited to weather condition as well as accessibility. As a result, an inspection service named SkyBEAM is proposed by Tremco that uses unmanned aerial vehicles (UAVs) equipped with high-resolution thermographic and video cameras to help locate where facilities may be leaking energy or may have wet insulation. Although the superiority of SkyBEAM is clear in terms of greater safety and access benefits, the quality of the results produced by UAVs compared to manual inspection methods needs to be investigated. The accuracy or the range of defects detected by UAVs is not yet known and requires further examination and field data analysis. This research aims at generating a robust data analysis framework to assess the value of SkyBEAM in terms of time savings, quality of inspection and scale of inspection. For this purpose, a study of the current alternative inspection methods in the industry including visual inspection and manual thermographic scanning is to be undertaken on four buildings. The data driven from the images taken by UAVs is then compared with the combination of images taken by the other two methods. The reports generated by each method will also be analyzed and compared in terms of accuracy of the location and the scale of the deficiencies detected as well as details provided for each. The data is then interpreted and analyzed and suggestions for improvements to the current state of the use of UAV in roofing inspection are offered. As a result, the proposed research enhances safety in inspection of buildings by providing improvements in details of inspection and also reporting of the current technologies. Using the improved inspection system provided in this research will enormously increase productivity of the inspection or condition assessment and decrease the associated negative risks.