DefectTR: End-to-end defect detection for sewage networks using a transformer

Experimentally driven evaluation methods of concrete sewers biodeterioration on laboratory-scale: A critical review

(2022) Szymon Madraszewski et al.   CONSTRUCTION AND BUILDING MATERIALS

Towards an automated condition assessment framework of underground sewer pipes based on closed-circuit television (CCTV) images

(2021) Mingzhu Wang et al.   TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY

Robust Korean License Plate Recognition Based on Deep Neural Networks

(2021) Hanxiang Wang et al.   SENSORS

A Multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN

(2021) Duo Ma et al.   CONSTRUCTION AND BUILDING MATERIALS

Crop pest recognition in natural scenes using convolutional neural networks

(2020) Yanfen Li et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Classifying cracks at sub-class level in closed circuit television sewer inspection videos

(2020) Xin Zuo et al.   AUTOMATION IN CONSTRUCTION

Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning

(2020) Mingzhu Wang et al.   AUTOMATION IN CONSTRUCTION

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

(2019) Xiangyang Ye et al.   Frontiers of Environmental Science & Engineering

Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification

(2019) Duanshun Li et al.   AUTOMATION IN CONSTRUCTION

A unified convolutional neural network integrated with conditional random field for pipe defect segmentation

(2019) Mingzhu Wang et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Improved non-maximum suppression for object detection using harmony search algorithm

(2019) Yanan Song et al.   APPLIED SOFT COMPUTING

Underground sewer pipe condition assessment based on convolutional neural networks

(2019) Syed Ibrahim Hassan et al.   AUTOMATION IN CONSTRUCTION

Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning

(2019) Qian Xie et al.   IEEE Transactions on Automation Science and Engineering

A deep learning-based framework for an automated defect detection system for sewer pipes

(2019) Xianfei Yin et al.   AUTOMATION IN CONSTRUCTION

Utilizing text recognition for the defects extraction in sewers CCTV inspection videos

(2018) L. Minh Dang et al.   COMPUTERS IN INDUSTRY

Single image dehazing and denoising combining dark channel prior and variational models

(2018) Zhi Wang et al.   IET Computer Vision

An integrated assessment approach to prevent risk of sewer exfiltration

(2018) Khalid Kaddoura et al.   Sustainable Cities and Society

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

(2018) Jack C.P. Cheng et al.   AUTOMATION IN CONSTRUCTION

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

DehazeNet: An End-to-End System for Single Image Haze Removal

(2016) Bolun Cai et al.   IEEE TRANSACTIONS ON IMAGE PROCESSING

Environmental life cycle analysis of pipe materials for sewer systems

(2016) Ehsan Vahidi et al.   Sustainable Cities and Society

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

(2015) Christian Koch et al.   ADVANCED ENGINEERING INFORMATICS

Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

(2013) Mahmoud R. Halfawy et al.   AUTOMATION IN CONSTRUCTION

Classification of defects with ensemble methods in the automated visual inspection of sewer pipes

(2013) Wei Wu et al.   PATTERN ANALYSIS AND APPLICATIONS