Sewer systems play a key role in cities to ensure public assets and safety. Timely detection of defects can effectively alleviate system deterioration. Conventional manual inspection is labor-intensive, error-prone and expensive. Object detection is a powerful deep learning technique that can complement and/or replace conventional inspection, especially in complex environments. This study compares two classic object-detection methods, namely faster region-based convolutional neural network (R-CNN) and You Only Look Once (YOLO), for the detection and localization of five types of sewer defects. Model performances are evaluated based on their detection accuracy and processing speed under parameterization impacts of dataset size and training parameters. Results show that faster R-CNN achieved higher prediction accuracy. Training dataset size and maximum number of epochs (MaxE) had dominant impacts on model performances of faster R-CNN and YOLO, respectively. The processing speed increased along with the increasing training data for faster R-CNN, but did not vary significantly for YOLO. The models' abilities to detect disjoint and residential wall were highest, whereas crack and tree root were more difficult to detect. The results help to better understand the strengths and weaknesses of the classic methods and provide a useful user guidance for practical applications in automated sewer defect detection.

下水道系统在确保公共资产和安全的城市中发挥着关键作用。及时发现缺陷可以有效缓解系统劣化。传统的人工检查是劳动密集型的，容易出错且昂贵。对象检测是一种强大的深度学习技术，可以补充和/或替代传统检测，尤其是在复杂环境中。本研究比较了两种经典的对象检测方法，即更快的基于区域的卷积神经网络 (R-CNN) 和 You Only Look Once (YOLO)，用于检测和定位五种类型的下水道缺陷。在数据集大小和训练参数的参数化影响下，基于其检测精度和处理速度评估模型性能。结果表明，更快的 R-CNN 实现了更高的预测精度。训练数据集大小和最大时期数 (MaxE) 分别对更快的 R-CNN 和 YOLO 的模型性能有主要影响。对于更快的 R-CNN，处理速度随着训练数据的增加而增加，但对于 YOLO 并没有显着变化。模型检测不相交和住宅墙的能力最高，而裂缝和树根更难检测。结果有助于更好地了解经典方法的优缺点，并为自动化下水道缺陷检测的实际应用提供有用的用户指导。检测脱节和住宅墙的能力最高，而裂缝和树根更难检测。结果有助于更好地了解经典方法的优缺点，并为自动化下水道缺陷检测的实际应用提供有用的用户指导。检测脱节和住宅墙的能力最高，而裂缝和树根更难检测。结果有助于更好地了解经典方法的优缺点，并为自动化下水道缺陷检测的实际应用提供有用的用户指导。