基于无人机巡检与深度学习的河道整治施工进度图像识别

Image recognition of river regulation construction progress based on UAV inspection and deep learning

长线性小流域治理工程中的河道衬砌、生态护岸等距离长、范围广、布置分散,且工区交通不便,人工巡检费时费力,难以及时掌握工程的整体施工进度形象面貌。提出了基于无人机巡检与深度学习的河道整治施工进度智能图像识别方法,通过定位施工节点(施工区域起点和终点)的位置计算施工进度。首先,建立了施工区域目标检测模型,针对无人机航拍影像进行河道衬砌护岸施工区域的识别以及施工节点的定位;然后,利用尺度不变特征变换(scale-invariant feature transform,SIFT)算法对不同视频帧中的施工节点进行匹配,并基于单目视觉的运动视差法,计算施工节点的实际工区坐标;最后,计算当前衬砌护岸施工进度,并分析进度偏差。结果表明:该方法得到的施工节点定位平均误差为1.026 m,平均相对误差为0.74%,该方法能够较为准确地从航拍图像中识别得到当前衬砌护岸的施工进程,从而实现长线性工区快速巡检,及时掌控现场施工进度,提高工程管理的智能化水平。

Long-line river regulation projects such as bank lining and revetment projects are far stretched and scattered with a wide range,resulting in inconvenient transportation for inspection.Besides,conventional manual inspection is time-consuming and labor-intensive,which is difficult to grasp the construction progress of the whole project in practice.In view of this,this paper proposes an intelligent construction progress monitoring method of river regulation projects based on unmanned air vehicle(UAV)inspection and deep learning,which can calculate the construction progress by locating construction nodes(i.e.,starting point and ending point of the construction area).Firstly,the object detection model of construction area is established to recognize the construction area of lining and revetment and locate the construction nodes based on UAV aerial photography.Then,SIFT(scale-invariant feature transform)algorithm is used to match the construction nodes in different video frames,and a motion parallax method based on monocular vision is introduced to locate the actual work area coordinates of construction nodes.Finally,the current lining construction progress is calculated and the progress deviation is analyzed.The results show that the average error of the construction progress is 1.026 m,and the average relative error is 0.74%,indicating that the proposed method can recognize the construction progress of lining and revetment accurately based on the UAV images,thereby achieving full coverage and rapid inspection of long-line projects,timely control of on-site construction progress,and improvement of the intelligent level of engineering management.