Inspection Of Overhead Power Line Corridor Obstacles By UAV Photogrammetry

The inspection of power lines is a critical factor for the safe operation of power transmission grids.To ensure the safe operation of power lines,it is necessary to ensure that a range of empty space without conductive objects exists around an extra high voltage(EHV)power line.Trees or tree branches that are too close to power lines should be trimmed,because vegetation is conductitve,which will lead to electric arcs.However,vegetation within the power line corridor will naturally grow after a power transmission grid becomes operational.A discharge may be generated when the distance between the vegetation and the power line is less than the safety threshold,thereby endangering the safe operation of the power transmission grid.As a result,the research of power line inspection across a large area has become a hot spot in recent years,whose main purpose is to detect possible obstacles within the corridor.Of all the possible means to carry out power line inspection,UAVs show a great potential due to the ability to perform 3D reconstructions for the power line corridor at low costs when equipped with lightweight digital cameras.Once stereo images of the power lines and the ground are taken during flights,they can be subjected to photogrammetric methods to give a 3D reconstruction result,which can then be used to automatically detect and measure possible obstacles through a spatial distance calculation.To use UAV to detect the obstacles within the power line corridor,three key problems need to be solved;3D reconstruction of the ground of the power line corridor,automatic power line measurement,and automatic recognition of obstacles.For ground reconstruction of the power line corridor,dense image matching techniques can be adopted to extract dense point clouds of the power line corridor.In UAV power line inspection,image-matching algorithms must be adapted to ground conditions with dense vegetation.At the same time,algorithms must be capable of extracting tree canopies and buildings stably and reliably.It is also difficult to use image-matching methods to find the corresponding points along a power line because of the small diameter of power lines and the complexity of their backgrounds.A 3D reconstruction of power lines can be conducted by the traditional manual stereo measurement method,which restricts the level of automation of inspection by using UAV images and which needs further improvement.In this paper,we propose an automatic inspection method for power lines using UAV images.This method,known as the power line automatic measurement method based on epipolar constraints(PLAMEC),acquires the spatial position of the power lines.Then,the semi patch matching based on epipolar constraints(SPMEC)dense matching method is applied to automatically extract dense point clouds within the power line corridor.Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground.The main researchcontents and innovative contributions are as follows:(1)Semi Patch Matching Algorithm Based on Epipolar ConstraintsIn our proposed method,SPMEC,epipolar images are the processing unit and a coarse to fine image matching strategy was adopted,under the initial parallax constraint,a large matching window searches a one-dimensional image along the epipolar line and is used to extract the coarse matching seed points.If a parallax is continuous within an object and the same object has a consistent texture in the image,then the coarse matching seed point is considered the center of the fine matching window.In the segmented image,based on the segmented object of the seed point,a patch matching constraint is constructed.Then,the initial parallax of the points to be matched within the semi patch is determined according to the geometric conditions of the semi patch.A one-dimensional search is conducted within a smaller search range.Finally,an outlier detection algorithm is applied to eliminate the mismatched points.The experimental results showed that dense point clouds of canopies and buildings with regular outlined can be extracted by SPMEC in rural areas with lush vegetation.A major improvement of the proposed method over most matching algorithms that based on a normalized CC(correlation coefficient)is that the value of the normalized CC as well as its curve characteristics are both used.Once the corresponding image point is found in the search region of the right image when sliding the matching window in the image correlating process,the CC will change following a law from small to large as the matching window gets closer to the corresponding point;and from large to small as the matching window moves far away.Isolated peaks are evident in the data;two sides of the peak value are approximately symmetrical.It's demonstrated by the experimental results that to get more dense matching points,successful matching rates of the curve characteristics of the CC can be improved without introducing too many mismatch points if the proposed method is used instead of simply lowering the threshold.Another crucial issue of dense image matching is the disparity compatibility between a matching point and its neighbor's.The left epipolar imageis segmented using Graph-Based image segmentation method.The experimental result shows in the road and rooftop of the point cloud obtained using semi patch,there are fewer holes,for which the initial parallax of the surrounding objects and homogenous textures in the match window are to be blamed,compared with the point cloud obtained without semi path.By determining the initial parallax of the match windowusing the corresponding segmentafter semi patch is applied,the successful match rate are increased in roads,rooftop areas as well as vegetation areas.(2)Power Line Automatic Measurement method based on Epipolar ConstrainsPLAMEC(Power Line Automatic Measurement method based on Epipolar Constrains)is proposed for automatically measuring power lines because of the necessity to locate ground points in the image pairs along the corresponding epipolar lines.The first step is to select a pair of images from two adjacent strips as the stereo image pair,which must be selected carefully to ensure a perpendicular relationship between the epipolar line and the direction of the power line.The coplanarity condition is then applied when generating the epipolar images while the relative orientation algorithm is used to derive the relative orientation parameters of the stereo image pair.After that,the 2D vectors of the power lines are obtained from the left and right epipolar images using the power line automatic extraction algorithm before finally extracting pairs of corresponding epipolar lines at a certain interval in the direction of the y parallax of the epipolar image.A pair of corresponding points on the power line is eventually extracted by intersecting the epipolar line with the two power line vectors in the left and right epipolar images respectively.It was showed by the results that automatic measurement of power lines was done at a success rate of 93.2%,which is as good as that of manual measurement.Besides,the method is further proved to be an appropriate substitution of manual measurements since the RMS error of the elevation differences is better than ±0.15 m between the two methods.(3)Automatic Detection of Obstacles within a Power Line CorridorThe extracted power line is taken as the bus line to construct a spatial buffer around at a safe distance,after which obstacles can be detected by intersecting the 3D point clouds against the spatial buffer zone.Asection,whose approximate length is 3.9 km?of a 220 kV power line was selected with the elevation variation along it being about 200 m;nine towers were found within the corridor and the safe distance threshold was set as 6.5 m.The result shows that eight true obstacles were detected and their distances to the power lines turned to be approximately consistent to those measured by field surveying.Besides,the distance difference between the two methods was better than±0.5 m while the obstacle inspection requirement was better than ±2m,which means the requirement was meted by the proposed method.