In the daily driving process, road potholes pose a great threat to traffic safety. However, the actual road potholes are often irregular and the background is complex. So, it is difficult to accurately measure the volume of potholes. In this paper, a method for measuring the volume of road potholes based on three-dimensional point clouds is presented. First, binocular vision is used to obtain 3D point cloud data of the pothole, and the segmented pothole point clouds are projected onto the coordinate plane established by the road surface. Then, we triangulate projection points on the coordinate plane. Finally, restore these points to the true elevation points to generate triangular prism, and calculate the volume of a single triangular prism one by one. The accuracy and effectiveness of this algorithm are verified by experiments.
Road potholes affect comfort, safety, traffic condition and vehicle stability. Accurately detecting these potholes is vital for assessing the degree of pavement distress and developing road maintenance plan accordingly. This paper proposes a simple and effective pothole detection method based on 3D point cloud segmentation. Using binocular stereo vision to acquire 3D point clouds, fitting the pavement plane and then eliminating it from the 3D point clouds of road scene, we could roughly extract the pothole. K-means clustering and region growing algorithms were adopted to extract the potholes precisely. The experimental results demonstrate that our proposed method has a very good segmentation effect on scenes involving plane and target object.
KEYWORDS: 3D modeling, Cameras, 3D vision, Calibration, Satellites, Reconstruction algorithms, Visual process modeling, Detection and tracking algorithms
In this paper, we propose an automatic method for high precision measurement and 3D reconstruction of non-cooperative spacecraft based on binocular vision. The Zhengyou Zhang’s calibration method was implemented to calibrate the camera’s internal and external parameters; the 8-point algorithm was adopted to compute the fundamental and essential matrix between the cameras; we got the relative position of binocular camera by the method of the SVD of essential matrix; a stereo matching algorithm depending on the Semi-Global Matching was adopted for disparity map. Subsequently, the cloud information of world points was calculated through least square method. In our experiment, a complex outdoor scene was used. We made a satellite model which has the same size of the real one. To get the accurate position and angle of the spacecraft, the ellipse marks on the spacecraft was exacted effectively under three constraints. The experimental results show that the spacecraft model can be reconstructed accurately by our method. The method contributes the error rate of 1% for the test length and 3% for the test angle in 1 meter.
In this paper, a road detection algorithm from the low illumination remote sensing images is proposed. First, the top-hat transform is used to enhance the edge information in low illumination images. Next, a road detection method based on parallel lines is proposed to detect the parallel characteristics of the two edges of the road. The experiment results show that the proposed algorithm can detect the road information effectively and precisely.
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