Improved YOLO11 for Maglev Train Foreign Object Detection
To address the issues of small target miss detection, false positives in complex scenarios, and insufficient real-time performance in maglev train foreign object intrusion detection, this paper proposes a multi-module fusion ... | Find, read and cite all the research you need on Tech Science Press
Authors
- Qingqing Fang (ORCID: https://orcid.org/0000-0002-0045-1157)
- Dong‐Liang Peng
- Lu Zeng (ORCID: https://orcid.org/0000-0001-9381-7690)
- Zixuan Jiang
Publication Details
- Journal
- Journal on artificial intelligence
- Published
- 2025-01-01
- DOI
- https://doi.org/10.32604/jai.2025.073016
- Primary Topic
- IoT and GPS-based Vehicle Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00