Papers
1
Total Citations
5
H-Index
1
About
Enli Lv is a researcher at the forefront of agricultural robotics and computer vision, specializing in intelligent monitoring systems for livestock management. Their primary research focuses on developing lightweight, efficient deep learning models for automated animal detection and counting in intensive farming environments. Lv's most notable contribution is a pioneering approach that integrates green inspection robots with advanced image segmentation methods to automatically count pigs in commercial piggeries. This work, published in 2025 and already garnering 5 citations, demonstrates significant practical impact. Lv's innovation lies in optimizing the YOLOv8n-seg framework by replacing the computationally heavy C2f module with the lightweight Ghost module in the backbone network, dramatically reducing model complexity without sacrificing accuracy. Additionally, they introduced a spatial group enhancement attention mechanism and a lightweight shared detail enhancement convolutional detection head, further improving detection precision while maintaining real-time performance. This work addresses critical challenges in precision livestock farming, enabling cost-effective, automated monitoring that reduces labor demands and improves animal welfare. Lv's research represents a meaningful step toward practical, deployable AI solutions in agriculture, bridging the gap between cutting-edge computer vision and real-world farming applications.
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Top Papers
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