一个有条件细分引导的网络,用于在闭塞下完成石榴图像
Duokuo Zhang1,2, Ruizhe Hou3, Jingjing Guo4
1School of Information Engineering, Henan Institute of Science and Technology, Hongqi, Xinxiang, 453003, Henan, China. zhangduokuo@stu.hist.edu.cn.
Plant methods
|November 27, 2025
概括
本研究介绍了有条件细分指导的扩散网络 (CSD-Net),以改善农业图像中的石榴果实检测. CSD-Net有效地重建了封闭的水果结构,提高了自动收获和产量估计的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 在农业图像中,叶子和树枝的遮蔽阻碍了准确的石榴产量估计和自动收获.
- 现有的图像完成方法在封闭的农业图像中扎着结构忠诚度.
研究的目的:
- 开发一种新的高保真图像完成和封闭石榴果实细分的新框架.
- 解决传统方法在恢复农业图像中的结构完整性的局限性.
主要方法:
- 提出了有条件细分引导的扩散网络 (CSD-Net),一种轻量级的统一条件扩散模型.
- 使用共享的编码器,分段分支和RGB扩散分支.
- 杆分段面具作为结构先验,以指导扩散生成过程,以准确重建.
主要成果:
- 与传统方法相比,CSD-Net实现了更高的性能,PSNR为30.37dB,SSIM为0.9490.
- 该模型展示了水果结构的高保真重建,具有空间和纹理一致性.
- 在117 MB的模型大小下,实现了高完成质量和推断效率之间的平衡.
结论:
- CSD-Net提供了一种新且有效的解决方案,以减轻农业视觉感知中的封闭问题.
- 拟议的有条件指导机制显著改善了封闭的石榴图像中的结构完整性恢复.
- 这项工作通过在具有挑战性的农业条件下增强视觉感知来推进自动收获和产量估计.
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