基于深度学习无人机影像道路实景三维修复

3D restoration of road real scene based on deep learning for UAV images

针对基于无人机倾斜影像实景三维重建中,移动目标对道路实景三维重建造成几何变形和纹理失真的问题,提出一种基于深度学习的无人机影像道路实景三维修复方法。首先,通过添加注意力机制的深度学习网络模型YOLOv8对影像中目标进行检测;其次,在得到影像对应目标标记的基础上,根据已生成三维Mesh模型中各三角面在可视影像集合中的投影位置,结合影像所标记目标的对应范围,统计各三角面的类别信息以此判定移动目标;最后,利用移动目标判定结果对移动目标造成的三维模型几何变形及纹理错误进行修复,实现道路实景三维重建。结果表明:改进的网络模型较YOLOv4、YOLOv5和YOLOv8模型,平均精度(mAP)值平均提升10.82%,移动目标判定准确率达97.43%。与流行国外商业软件相比,所提方法重建修复效果更佳,自动化程度更高。

In order to address the problems of geometric deformation and texture distortion caused by moving targets on the road 3D Real Scene model based on UAV oblique photography,a restoration method of the road 3D Real Scene model is proposed using deep learning.Firstly,the YOLOv8 network,involving the attention mechanism,was employed to detect objects in the image.Secondly,based on the detected object’s range,the presence of moving targets was determined by analyzing the category information of each triangular face in the mesh,according to its projection position in the visual image set.Finally,the geometric deformation and texture distortion of the 3D model were restored from the results of moving targets determination to achieve the road 3D Real Scene model reconstruction.The results indicate that the improved network enhances the mAP by an average of 10.82%over YOLOv4,YOLOv5 and YOLOv8.Furthermore,the accuracy of moving targets determination is 97.43%.Additionally,in contrast to commercial software,the proposed method demonstrates a superior restoration effect and a higher level of automation.