动态编码网络用于在低可见度农业场景中强大的果实检测
Hanyun Lu1,2, Teng Jin1,2, Chen Wan1,2
1Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, China.
Frontiers in plant science
|November 12, 2025
概括
本研究介绍了动态编码网络 (DCNet),用于在具有挑战性的低可见性农业环境中进行强大的果实检测. DCNet显著提高了智能果园管理和机器人收获的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器人技术 机器人技术 机器人技术
背景情况:
- 精确的果实检测对于自动化农业至关重要,但低可见性条件 (雾,雨,低光) 会降低现有模型的性能.
- 智能果园管理和机器人收获需要强大的果实检测系统,可以克服环境挑战.
研究的目的:
- 开发一种新的模块化检测框架,即动态编码网络 (DCNet),用于在低可见度农业场景中增强果实检测.
- 解决当前模型在视觉上具有挑战性的环境中的性能退化问题.
主要方法:
- 拟议的DCNet框架有四个关键组件:动态特征编码器,全球注意门,交叉注意解码器和代特征注意.
- 使用了LVScene4K数据集,其中包括各种水果 (葡萄,果,子等). 在各种不利条件下 (雾,雨,低光,遮蔽).
主要成果:
- 在LVScene4K数据集上,DCNet实现了86.5%的平均平均精度 (mAP) 和84.2%的交叉与联合 (IoU).
- 超越了最先进的方法,F1得分为3.4%,IOU改进率为4.3%.
- 在RTX 3090 GPU上保持了28 FPS的实时推断速度.
结论:
- DCNet为实时农业机器人部署提供了精度和计算效率的卓越平衡.
- DCNet的模块化架构表明了在不同作物和复杂的农业环境中普遍化的潜力.
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