A5057821511- Industrial Vision Systems and Defect Detection
- Advanced Neural Network Applications
- Digital Imaging for Blood Diseases
- Image Processing Techniques and Applications
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Medical Imaging Techniques and Applications
- Infrastructure Maintenance and Monitoring
- Image Enhancement Techniques
- COVID-19 diagnosis using AI
Yibin University
2024
Gannan Normal University
2022
Abstract With the rapid development of power industry, higher requirements have been put forward for real-time monitoring and fault identification equipment. However, images equipment in actual scenes are often affected by problems such as uneven illumination color distortion, leading to a decrease performance target detection model. Hence, this paper suggests merging Multi-Scale Retinex with Color Restoration (MSRCR) algorithm YOLO-v8 model enhance visual quality boost accuracy efficiency...
Corrosion defects will increase the risk of power equipment failure, which directly affect stable operation systems. Although existing methods can detect corrosion equipment, these are often poor in real-time. This study presents a two-stage detection approach that combines YOLOv8 and DDRNet to achieve real-time precise area localization. In first stage, network is used identify locate substation detected ROI areas passed second stage for semantic segmentation. To enhance performance both...
To propose a new method for fast MRI reconstruction based on deep learning in parallel data using loss function defined as the summation of mean squared errors magnitude and phase.The multicoil image were combined into single-coil to eliminate correlation between noises used label training process. Considering importance phase information some applications, where was lost when combining sum square method, introduced, weighted error (MSE) phase. The single weight balance different...