.png){kind=icon}
Return
Automatic sewer pipe defect semantic segmentation based on improved U-Net
.png){kind=icon}
PRE
AI
DOI：10.1016/j.autcon.2020.103383.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
At present, technologies based on deep learning methods for automated detection of sewer defects have been developing rapidly. In this study, a novel semantic segmentation network called PipeUNet is proposed for sewer defect segmentation. In order to enhance the feature extraction capability and resolve semantic differences between high level and low level features, a new module named feature reuse and attention mechanism block is added between the original skip connections of U-Net. Focal loss is adopted to solve the class imbalance problem. PipeUNet was trained using the CCTV images with typical defects including crack, infiltration, joint offset and intruding lateral. It was tested by the defects images and normal images to evaluate the network's defect segmentation and detection performance respectively. It achieved the highest Mean Intersection over Union of 76.37% which proved the proposed approach's efficiency. It can process CCTV images at a high speed of 32 images per second.
Keywords:
Sewer inspection
Semantic segmentation
Deep learning
U-Net closed-circuit television
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.5
Papers:
6.3K
Citations:
4.2W
Organization
Cited Papers
A systematic study of the class imbalance problem in convolutional neural networks.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAIShareSaveElectrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THVRSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveApplication of Morphological Segmentation to Leaking Defect Detection in Sewer PipelinesSENSORSOAAI
SENSORSIF3.5
OAAIShareSave
View more