Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea.
Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2022 Apr 1;22(7):2722. doi: 10.3390/s22072722.
Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.
由于基于视觉的分析技术具有经济、安全和高效的优势，最近得到了显著的发展，能够广泛应用于自主施工。尽管目前已经有许多关于地下污水管道缺陷检测和状况评估的研究，但我们仍然缺乏对最新发展的全面综述。本调查对不同的污水检查算法进行了系统分类，将其分为缺陷分类、缺陷检测和缺陷分割三类。在回顾了过去 22 年中与污水缺陷检查相关的研究后，根据提出的技术分类法，详细组织和讨论了主要的研究趋势。此外，还描述和解释了引用文献中使用的不同数据集和评估指标。此外，还从处理精度和速度两个方面报告了最新方法的性能。
