A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A deep learning-based framework for an automated defect detection system for sewer pipes
Abstract The municipal drainage system is a key component of every modern city's infrastructure. However, as the drainage system ages its pipes gradually deteriorate at rates that vary based on the conditions of utilisation (i.e., intrinsic conditions) and other extrinsic factors such as the presence of trees with deep roots or the traffic load above the sewer lines, which collectively can impact the structural integrity of the pipes. As a result, regular monitoring of the drainage system is extremely important since replacement is not only costly, but, more importantly, can disturb the daily routines of citizens. In this respect, closed-circuit television (CCTV) inspection has been widely accepted as an effective inspection technology for buried infrastructure. Since sewer pipes can run for thousands of kilometers underground, cities collect massive amounts of CCTV video footage, the assessment of which is time-consuming and may require a large team of trained technologists. A framework is proposed to realize the development of a real-time automated defect detection system that takes advantage of a deep-learning algorithm. The framework focuses on streamlining the information and data flow, proposing patterns of input and output data processing. With the development of deep learning techniques, a state-of-the-art convolutional neural network (CNN) based object detector, namely YOLOv3 network, has been employed in this research. This algorithm is known to be very efficient in the field of object detection from the perspective of processing speed and accuracy. The model used in this research has been trained with a data set of 4056 samples that contains six types of defects (i.e., broken, hole, deposits, crack, fracture, and root) and one type of construction feature (tap). The performance of the model is validated with a mean average precision (mAP) of 85.37%. The proposed output of the system includes labeled CCTV videos, frames that contain defects, and associated defect information. The labeled video can serve as the benchmark for assessment technologists while the multiple output frames provide an overview of the condition of the sewer pipe.
Highlights A deep learning-based framework is proposed for defect detection system. Input data, implementation process and output products are streamlined. Using videos as the processing objects instead of images in former research YOLOv3 algorithm is used as the object detector and proved to be efficient. Outputs are labeled videos, defects frames, and associated defects information.
A deep learning-based framework for an automated defect detection system for sewer pipes
Abstract The municipal drainage system is a key component of every modern city's infrastructure. However, as the drainage system ages its pipes gradually deteriorate at rates that vary based on the conditions of utilisation (i.e., intrinsic conditions) and other extrinsic factors such as the presence of trees with deep roots or the traffic load above the sewer lines, which collectively can impact the structural integrity of the pipes. As a result, regular monitoring of the drainage system is extremely important since replacement is not only costly, but, more importantly, can disturb the daily routines of citizens. In this respect, closed-circuit television (CCTV) inspection has been widely accepted as an effective inspection technology for buried infrastructure. Since sewer pipes can run for thousands of kilometers underground, cities collect massive amounts of CCTV video footage, the assessment of which is time-consuming and may require a large team of trained technologists. A framework is proposed to realize the development of a real-time automated defect detection system that takes advantage of a deep-learning algorithm. The framework focuses on streamlining the information and data flow, proposing patterns of input and output data processing. With the development of deep learning techniques, a state-of-the-art convolutional neural network (CNN) based object detector, namely YOLOv3 network, has been employed in this research. This algorithm is known to be very efficient in the field of object detection from the perspective of processing speed and accuracy. The model used in this research has been trained with a data set of 4056 samples that contains six types of defects (i.e., broken, hole, deposits, crack, fracture, and root) and one type of construction feature (tap). The performance of the model is validated with a mean average precision (mAP) of 85.37%. The proposed output of the system includes labeled CCTV videos, frames that contain defects, and associated defect information. The labeled video can serve as the benchmark for assessment technologists while the multiple output frames provide an overview of the condition of the sewer pipe.
Highlights A deep learning-based framework is proposed for defect detection system. Input data, implementation process and output products are streamlined. Using videos as the processing objects instead of images in former research YOLOv3 algorithm is used as the object detector and proved to be efficient. Outputs are labeled videos, defects frames, and associated defects information.
A deep learning-based framework for an automated defect detection system for sewer pipes
Yin, Xianfei (author) / Chen, Yuan (author) / Bouferguene, Ahmed (author) / Zaman, Hamid (author) / Al-Hussein, Mohamed (author) / Kurach, Luke (author)
2019-09-22
Article (Journal)
Electronic Resource
English
A Deep Learning Based Automated Structural Defect Detection System for Sewer Pipelines
| British Library Conference Proceedings | 2019
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| ASCE | 2025