Kanglin Yang
Papers
1
Total Citations
2
H-Index
1
About
Kanglin Yang’s research lies at the intersection of computer vision and sustainable engineering, with a focus on applying deep learning to real-world environmental challenges. Their most cited work, “An application case of object detection model based on Yolov3-SPP model pruning” (2022), tackles the pressing issue of municipal solid waste classification. By pruning the YOLOv3-SPP architecture, Yang developed a more efficient object detection model that reduces computational overhead while maintaining accuracy—a critical step toward automating waste sorting. This innovation addresses the high labor costs and health risks of manual sorting, offering a scalable solution for resource recovery and pollution reduction. Though early in their career, with 2 citations to date, Yang’s work demonstrates a clear commitment to using AI for societal benefit, bridging the gap between cutting-edge model optimization and practical environmental impact. Their research signals a promising trajectory in applied machine learning, where algorithmic efficiency meets urgent sustainability needs.
Research Focus
Key Achievements
Top Papers
- 1