A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fault Detection and Identification Based on Image Processing and Deep Learning
Fault detection approaches based on computer vision have been utilized in a variety of applications, including maintaining high quality control in industrial manufacturing processes and detecting defects in inaccessible or isolated systems. However, various systems have been utilized for each application. In this research, we present a fault detection approach based on computer vision and deep learning that can be used for a variety of applications. We tested our model on three distinct datasets of different engineering problems. A MobileNetV2 based convolutional neural network model was applied on different engineering problems related photovoltaic solar, manufacture of magnetic tiles and electrical commuter systems. The results obtained for these three cases are: 85 percent, 92 percent, and 92 percent, respectively.
Fault Detection and Identification Based on Image Processing and Deep Learning
Fault detection approaches based on computer vision have been utilized in a variety of applications, including maintaining high quality control in industrial manufacturing processes and detecting defects in inaccessible or isolated systems. However, various systems have been utilized for each application. In this research, we present a fault detection approach based on computer vision and deep learning that can be used for a variety of applications. We tested our model on three distinct datasets of different engineering problems. A MobileNetV2 based convolutional neural network model was applied on different engineering problems related photovoltaic solar, manufacture of magnetic tiles and electrical commuter systems. The results obtained for these three cases are: 85 percent, 92 percent, and 92 percent, respectively.
Fault Detection and Identification Based on Image Processing and Deep Learning
Jarndal, Anwar (author) / Mahmoud, Mahamad Salah (author) / Mohammad Abbas, Omar (author)
2022-02-21
1990271 byte
Conference paper
Electronic Resource
English
Deep CNN-based concrete cracks identification and quantification using image processing techniques
| Springer Verlag | 2023
Advancing Fault Detection in Building Automation Systems through Deep Learning
| DOAJ | 2024
Detection of Bridge Damages by Image Processing Using the Deep Learning Transformer Model
| DOAJ | 2023