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- 摘要
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- 实验方案
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Image-Translation-Based Road Marking Extraction From Mobile Laser Point Clouds
摘要： Road markings are one of the most important safety elements in a road network, and they play a critical role in traffic safety. However, the automatic extraction of road markings remains a technical challenge in the fields of smart city construction and automatic driving. This paper presents an image-translation-based method of obtaining the 3D vectors of typical road markings from mobile laser point clouds. First, ground roughness is used as a criterion to extract ground points based on the topological relationship of adjacent scan lines, and the feature images of a road surface are generated using the adapted inverse distance weighted method. Second, by comparing objective functions based on the pix2pix framework, a finely adjusted image-to-image translation model named P2P_L1 is proposed for the segmentation of road markings. The proposed model outperforms the advanced DeepLab V3+ network in terms of precision, F1-score, and mean Intersection over Union indicators in the comparative segmentation results of ten types of road markings in the Shenzhen test area. Third, methods such as node averaging and optimized iterative closest point are developed for the 3D vectorization of road markings. This study presents a new approach for the automatic extraction of road markings to provide effective technical support for the construction of smart cities.
关键词： image translation,segmentation,road marking,Conditional generative adversarial nets (cGANs),laser point cloud
更新于2025-09-23 15:19:57
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Automatic road-marking detection and measurement from laser-scanning 3D profile data
摘要： Automatic road-marking detection and measurement have great significance for pavement maintenance and management. Laser-scanning 3D profile data provide a new way of road-marking detection and measurement with an elevation accuracy of about 0.25 mm. This paper presents an automatic road-marking detection and measurement method that uses laser scanning of 3D pavement data. The elevation characteristics and geometric statistics that characterize road markings have been fully analyzed using 3D data. The first step was to use a specially designed step-shaped operator to convolve profile data to identify the regions of suspected marking edges at the profile level, which helps reduce the influence of other pavement factors, including crosswise-slope information, cracks, and rutting. Next, by combining the geometric characteristics of the road-marking region and the continuity of the convolution features at image level, the regions of suspected 3D road markings were extracted. Third, a convolutional neural network was introduced to distinguish real-marking data more clearly. Finally, the three-dimension measurement information was extracted from the detected region and from elevation information. Road-marking recognition experiments were then conducted based on real measured 3D data. The detection accuracies were all greater than 90.8% for 4178 test samples from five road sections with different kinds of road markings. Furthermore, the repeatability of multiple measurement results for road-marking elevations from two selected road sections was about 95%, and the correlation of the obtained road-marking elevations with manually measured elevations was about 85.36% for 200 measurement points.
关键词： Road-marking detection,Laser scanning,Convolution,Three-dimension measurement information,Convolutional neural network
更新于2025-09-16 10:30:52