FreqDyn-YOLO:一种高性能多级特征融合算法,用于在农田中检测塑料薄膜残留物
Mingyang Zhang1, Jianjie Zhang2, Yihang Peng2
1College of Software, Xinjiang University, Urumqi 830091, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
一个新的FreqDyn-YOLO模型可以准确地检测田间的塑料碎片,从而提高农业的可持续性. 这项技术有助于回收和回收剩余塑料薄膜,解决农业环境问题.
科学领域:
- 农业工程
- 计算机视觉
- 环境科学
背景情况:
- 塑料覆盖面对于农业生产至关重要, 但会留下残留的碎片, 阻碍作物生产和环境可持续性.
- 由于与土壤的视觉相似性,不规则的形状和可变的大小,精确检测和识别残留的塑料片在技术上具有挑战性.
研究的目的:
- 开发一个先进的检测模型,FreqDyn-YOLO,用于识别农业环境中的残留塑料薄膜.
- 增强塑料片和土壤背景之间的区别.
- 改进各种尺度的检测性能,并适应各种膜形态.
主要方法:
- 基于YOLO11架构开发了FreqDyn-YOLO.
- 引入了一个频率-C3k2 (FreqC3) 模块,具有频率特征转移注意力 (FreqFTA),用于增强特征提取.
- 实施高性能多级特征金字塔网络 (HPMSFPN) 以实现有效的跨层特征融合.
- 集成了具有DCNv4 (DWD4) 的动态检测头,以适应不同的薄膜形状和计算效率.
主要成果:
- 与定制农业数据集的基线方法相比,FreqDyn- YOLO在mAP50中取得了5. 37%的改进,精度为1. 97%,回忆率为2. 96%.
- 该模型与其他近期检测方法相比表现优异.
- 实验结果验证了该模型在识别残留塑料薄膜方面的有效性.
结论:
- FreqDyn-YOLO为农业中精确的残留塑料薄膜识别提供了坚实的技术基础.
- 开发的模型显示了将其整合到剩余覆盖物回收和回收的自动化系统中的巨大潜力.
- 这项研究通过解决塑料杂残留的挑战,为可持续的农业实践做出了贡献.
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