Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows
Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection...
Main Authors: | Paulius Palevičius, Mayur Pal, Mantas Landauskas, Ugnė Orinaitė, Inga Timofejeva, Minvydas Ragulskis |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/10/3662 |
Similar Items
-
An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows
by: Mayur Pal, et al.
Published: (2021-12-01) -
Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
by: Ugnė Orinaitė, et al.
Published: (2023-06-01) -
Efficient Detection and Measurements of Bridge Crack Widths Based on Streamlined Convolutional Neural Network
by: Yingjun Wu, et al.
Published: (2025-01-01) -
Tidal Effects on the Schumann Resonance Amplitudes Recorded by the Global Coherence Monitoring System
by: Ugnė Orinaitė, et al.
Published: (2024-04-01) -
MixSegNet: A Novel Crack Segmentation Network Combining CNN and Transformer
by: Yang Zhou, et al.
Published: (2024-01-01)