Danhao Wang
Papers
1
Total Citations
2
H-Index
1
About
Danhao Wang is a researcher specializing in industrial inspection and deep learning applications for power plant infrastructure. His primary research areas include computer vision, object detection, and pipeline leakage monitoring. Wang’s most notable contribution is the development of an improved Faster R-CNN framework for leakage detection and identification in power plant pipelines, addressing critical safety concerns in steam and oil systems. In this work, he enhanced the standard architecture by replacing VGG16 with ResNet101 for richer semantic feature extraction, and integrated a Convolutional Block Attention Module (CBAM) to boost detection accuracy. While his 2022 paper has garnered 2 citations to date, the work represents a practical advancement in automated industrial safety, demonstrating how deep learning can be tailored for real-world infrastructure monitoring. Wang’s approach offers a systematic solution for early leak detection, potentially reducing environmental hazards and operational downtime in power generation facilities. His research sits at the intersection of artificial intelligence and industrial engineering, with clear applications for smart maintenance systems.
Research Focus
Key Achievements
Top Papers
- 1