在鱼眼图像中使用深度学习和梯度增强进行像素智能的天空障碍细分.
Némo Bouillon1, Vincent Boitier1
1LAAS-CNRS, Université de Toulouse, CNRS, 31400 Toulouse, France.
Journal of imaging
|December 24, 2025
概括
本研究介绍了一种低成本的方法,用于在鱼眼图像中对天空和障碍物进行细分,这对于太阳能应用至关重要. 该框架使用合成数据和深度学习,以随时可用的硬件实现高度准确的结果.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 可再生能源技术可再生能源技术
背景情况:
- 准确的天空障碍物细分对于太阳能预测和环境监测至关重要.
- 目前的方法是昂贵的,需要特定的训练数据,并产生不精确的界限,经常忽视鱼眼透镜光学.
研究的目的:
- 开发一个低成本,准确的半球天空细分框架用于鱼眼图像.
- 为了提高在不同成像设备和环境中的稳定性和通用性.
主要方法:
- 从街景全景图像生成合成鱼眼训练图像.
- 透镜感知数据增强以模拟鱼眼投影和光度效应.
- 一个混合深度学习管道,将卷积神经网络 (CNN) 和梯度增强决策树 (GBDT) 结合起来,用于边界精细化.
主要成果:
- 在使用智能手机和低成本镜头的真实鱼眼图像上 (IoU: 96.63%,F1: 98.29%) 实现了高精度.
- 在外部全景数据集上展示了强大的跨数据集概括性.
- 混合CNN-GBDT方法有效地提高了天空障碍的界限.
结论:
- 拟议的框架为半球天空细分提供了一个准确的,具有成本效益的解决方案.
- 通过广泛部署的成像技术,使太阳能和环境监测的实际应用成为可能.
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