[HTML][HTML] Improving Sewer Damage Inspection: Development of a Deep Learning Integration Concept for a Multi-Sensor System

JT Jung, A Reiterer - Sensors, 2024 - mdpi.com
The maintenance and inspection of sewer pipes are essential to urban infrastructure but
remain predominantly manual, resource-intensive, and prone to human error …

Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network

D Tabernik, M Šuc, D Skočaj - Construction and Building Materials, 2023 - Elsevier
Automated quality control of pavement and concrete surfaces is essential for maintaining
structural integrity and consistency in the construction and infrastructure industries. This …

An efficient wastewater collection model for groundwater resource protection in smart cities

J Balamurugan, N Kasthuri, JS Sudarsan… - Groundwater for …, 2024 - Elsevier
Efficient wastewater collection is a critical concern in the development of smart cities, where
sustainable urban infrastructure is paramount. In the context of smart city wastewater …

[HTML][HTML] Co-CrackSegment: a new collaborative deep learning framework for pixel-level semantic segmentation of concrete cracks

NF Alkayem, A Mayya, L Shen, X Zhang, PG Asteris… - Mathematics, 2024 - mdpi.com
In an era of massive construction, damaged and aging infrastructure are becoming more
common. Defects, such as cracking, spalling, etc., are main types of structural damage that …

CDD-TR: Automated concrete defect investigation using an improved deformable transformers

M Dang, H Wang, TH Nguyen, L Tightiz, LD Tien… - Journal of Building …, 2023 - Elsevier
Public infrastructures, such as bridges, dams, and buildings, play a key role in urban
development. Structural inspection by visually monitoring and inspecting the structures for …

Classification of sewer pipe defects based on an automatically designed convolutional neural network

Y Wang, J Fan, Y Sun - Expert Systems with Applications, 2025 - Elsevier
Accurate classification of sewer pipe defects allows for timely prevention of system failures,
preventing environmental pollution caused by sewage leakage, thereby safeguarding public …

PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects

M Li, M Li, Q Ren, H Li, L **ao, X Fang - Applied Soft Computing, 2024 - Elsevier
With the continuous development of urbanization, the service life of sewer pipes is gradually
approaching a critical threshold. Defects within pipe networks can significantly affect the …

Semi‐supervised pipe video temporal defect interval localization

Z Huang, G Pan, C Kang, YZ Lv - Computer‐Aided Civil and …, 2024 - Wiley Online Library
In sewer pipe closed‐circuit television inspection, accurate temporal defect localization is
essential for effective pipe assessment. Industry standards typically do not require time …

A lightweight multi scale fusion network for IGBT ultrasonic tomography image segmentation

M Song, Z Wang, Y Chen, Y Li, Y **, B Jia - Scientific Reports, 2025 - nature.com
Abstract The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor
device, and the integrity of its internal structure directly influences both its electrical …

Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China

Q Zhou, Z Situ, W Feng, H Liu, X Liao, J Zhang… - Journal of …, 2024 - Elsevier
Deep learning techniques have offered innovative and efficient tools for accurate and
automated detection of sewer defects by leveraging large-scale sewer data and advanced …