MSFN-YOLOv11:基于改进的YOLOv11的新型多尺度特征融合识别模型,用于湿地生态系统中的鸟类实时监测
Linqi Wang1,2, Lin Ye1, Xinbao Chen1
1School of Earth Sciences and Spatial Information Engineering, Hunan University of Sciences and Technology, Xiangtan 411201, China.
Animals : an open access journal from MDPI
|December 11, 2025
概括
这项研究引入了MSFN-YOLO11,一个改进的鸟类物种识别模型,可以在杂,封闭的环境中提高准确性. 该模型为生物多样性监测提供了更快的培训和有效的实时视频处理.
科学领域:
- 生态与保护科学 生态与保护科学
- 计算机视觉和机器学习
- 环境监测 环境监测
背景情况:
- 智能鸟类物种识别对于生物多样性监测和保护至关重要.
- 复杂的自然环境带来了诸如阻塞和成像噪声等挑战,降低了识别准确度.
- 现有的模型与现实世界条件作斗争,需要改进的检测算法.
研究的目的:
- 开发一种改进的鸟类识别模型,在具有挑战性的环境条件下提高准确性和稳定性.
- 引入一个新的多尺度特征融合 (MSFN) 模块,集成到YOLOv11n架构中.
- 为了评估模型的性能在一个定制的数据集模拟现实世界的遮蔽和噪音.
主要方法:
- 选择YOLOv11n作为基线模型,将其与YOLOv8n进行比较.
- 通过拟议的MSFN模块增强了YOLOv11n骨干,利用并行扩展卷积和通道注意力.
- 创建了一个由十种鸟类的4540张图像 (6824个样本) 组成的自建数据集,并增加了遮蔽和各种噪音类型 (高斯式,波桑式).
主要成果:
- 在测试组中,MSFN-YOLO11模型实现了96.4%的平均平均精度 (mAP) @50和83.2%的mAP@50-95.
- 与原来的YOLOv11相比,在mAP@50实现了0.3%的改进,培训时间减少了18%.
- 在实时视频处理中证明了实际有效性,在 1920 × 1080 @ 72fps 上达到 63.1% 的精度.
结论:
- 拟议的MSFN-YOLO11模型提供了强大而准确的鸟类物种识别,即使有遮蔽和噪音.
- 该模型为湿地生态系统的实时鸟类监测提供了重要的技术支持.
- 增强的识别能力有助于改善危鸟类的保护工作.
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