Research on Sika Deer Behavior Recognition Based on YOLOv11 Lightweight SDB-YOLO Model for Small Sample Learning
He Gong, Zuoqi Wang, Jinghuan Hu, Yan Li, Longyan Liu, Yanhong Yu, Juanjuan Fan, Ye Mu · 2025
Read Research on Sika Deer Behavior Recognition Based on YOLOv11 Lightweight SDB-YOLO Model for Small Sample Learning by He Gong, Zuoqi Wang, Jinghuan Hu, Yan Li, Longyan Liu, Yanhong Yu, Juanjuan Fan, Ye Mu (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12784704.txt.
Description
He Gong and seven co-authors build a lean deer act spot tool for farms with few training shots. Their study appeared in twenty twenty-five. Sika deer acts are hard to read from pens due to light blocks and few shots. Their tool, SDB-YOLO, tweaks a known lean spot net with shared blend, ghost traits, and gaze parts.
A light head swap made training more firm. The tool hit a high truth mark at low math cost, a clear gain over the base net. Tests prove the tweaks help with small shot sets and cross-scale acts. The team backs it for smart farm watch of rare stock.
Catalog record
| CREATOR | He Gong, Zuoqi Wang, Jinghuan Hu, Yan Li, Longyan Liu, Yanhong Yu, Juanjuan Fan, Ye Mu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |