Research on Coal Gangue Image Recognition Method Based on EMAM-YOLO
Ying Jia, Baoshan Li, Yongxing Du, Wenxiang Zheng, Yu Li, Shiyan Wu · 2026
Read Research on Coal Gangue Image Recognition Method Based on EMAM-YOLO by Ying Jia, Baoshan Li, Yongxing Du, Wenxiang Zheng, Yu Li, Shiyan Wu (2026) 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-13191532.txt.
Description
Ying Jia and co-authors teach a vision model to spot coal waste rock in a 2026 paper. Underground mines fight dim light, coal dust, and odd rock shapes, all of which blur detection. Their model builds on a compact YOLO version, tuned for live use. A light backbone network cuts the model's size while keeping feature quality.
An attention pyramid module strengthens how features at different scales talk to each other. Picking waste rock from coal on the belt matters for both fuel quality and safety. A fast, sturdy detector could run at the belt without slowing the line. The rebuild aims at accuracy that survives the mine's harsh imaging conditions.
Catalog record
| CREATOR | Ying Jia, Baoshan Li, Yongxing Du, Wenxiang Zheng, Yu Li, Shiyan Wu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |