Erjiang Qi
Papers
1
Total Citations
2
H-Index
1
About
Erjiang Qi is a researcher specializing in industrial safety monitoring and deep learning applications for power plant infrastructure. His work focuses on developing advanced computer vision techniques for automated leakage detection and identification in critical pipeline systems. Qi’s most notable contribution is the development of an improved Faster RCNN framework that integrates Convolutional Block Attention Modules (CBAM) for enhanced detection of steam and oil leaks in power plant pipelines. By replacing the traditional VGG16 backbone with ResNet101, his approach achieves richer semantic feature extraction, significantly improving detection accuracy in complex industrial environments. This work, published in 2022, has garnered 2 citations and represents a practical advancement in applying deep learning to real-world industrial safety challenges. Qi’s research bridges the gap between state-of-the-art object detection algorithms and the pressing need for reliable, automated monitoring systems in power generation facilities. His contributions are particularly valuable for reducing manual inspection risks and enabling early warning systems for hazardous leaks, demonstrating the potential of AI-driven solutions in critical infrastructure maintenance and safety management.
Research Focus
Key Achievements
Top Papers
- 1