Integration of Multiframe and Multichannel Residual Attention Models for Mine Water Hazard Video Feature Recognition
Shuai Zhang, Yuxian Liu, Chengming Wang, Yintao Wei, Huaidong Luo, Huibin Sheng, Yuanze Du, Runsheng Wang, Zhiguo Wu, Jingbing Liu · 2025
Read Integration of Multiframe and Multichannel Residual Attention Models for Mine Water Hazard Video Feature Recognition by Shuai Zhang, Yuxian Liu, Chengming Wang, Yintao Wei, Huaidong Luo, Huibin Sheng, Yuanze Du, Runsheng Wang, Zhiguo Wu, Jingbing Liu (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-12756738.txt.
Description
Floodwater bursting into a coal mine is a disaster that cameras might catch early, if software can read the scene. Shuai Zhang and co-authors built a video model for that recognition task. Their design averages several frames to expose change, then lets attention pull the moving water into focus. A U-shaped network, upgraded with attention blocks, marks the inrush area. The middle frame of each clip serves as the teaching label.
Training data for mine hazards are scarce and scenes are messy, so the change-based design matters. Tests beat standard segmentation networks and generalized better. Early automatic warning could buy miners the minutes that manual watchers and sensors miss. The authors frame the model as a safety layer, not a replacement for drainage work.
Catalog record
| CREATOR | Shuai Zhang, Yuxian Liu, Chengming Wang, Yintao Wei, Huaidong Luo, Huibin Sheng, Yuanze Du, Runsheng Wang, Zhiguo Wu, Jingbing Liu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |