Research on Identification of Road Features from Point Cloud Data Using Deep Learning

Umehara, Yoshimasa; Tsukada, Yoshinori; Nakamura, Kenji; Tanaka, Shigenori; NAKAHATA, Koki

doi:10.20965/ijat.2021.p0274

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…It can be seen that 3D point cloud registration is a particularly key part of point cloud data processing. The dynamic registration algorithm is mainly aimed at the sampled data points of motion or deformation [22][23][24]. The usual method is to fix the three-dimensional laser scanner, and then let the threedimensional object to be scanned rotate along its own central axis.…”

Section: : ð8þmentioning

confidence: 99%

3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Liu¹,

2023

Wireless Communications and Mobile Computing

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Point cloud data registration is one of the key steps in 3D laser scanning data processing. At present, point cloud data registration has the problems of error and is too much time-consuming. In order to solve the above problems, a 3D point cloud data registration algorithm based on augmented reality technology is proposed, and the 3D data registration model is constructed by combining augmented reality technology, build the evaluation index of 3D point cloud data registration, and carry out the initial registration of 3D point cloud based on the evaluation index. The experiment shows that the 3D point cloud data registration algorithm based on augmented reality technology can more effectively improve the accuracy of data registration and avoid the problem of low efficiency of data registration in practice.

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Section: : ð8þmentioning

confidence: 99%

3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Liu¹,

2023

Wireless Communications and Mobile Computing

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“…Other studies propose workflows to detect simultaneously multiple road features. Umehara et al (2021) for example, propose a two-step approach. In the first step, after a segmentation of the ground, buildings and utility lines, the remaining point cloud is divided into individual road features.…”

Section: Road Features Inventorymentioning

confidence: 99%

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Barçon¹,

Landes

Grussenmeyer

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Dense point clouds acquired with a mobile laser scanning system (MLS) device become usual raw data for different surveyor applications: topographic maps, 3D models, road inventories, risk assessment of vegetation on road or railroads. Thanks to important evolutions in technologies, MLS devices became powerful and very popular. In the meantime, the need for point cloud automatic processing tools is growing. However, the available tools have not yet reached a sufficient level of maturity. Using MLS point clouds to produce topographic maps, BIM model or other deliverables, requires very often manual vectorization (or digitalization) work. In the road context, the transition from point cloud to road map that consists in delineating curb or road edges, road markings, pole, trees, facades etc. is currently performed manually. To reduce these time-consuming operations, several solutions have been proposed in the literature. In this paper we present the first results of a method consisting in vectorizing urban point cloud scene. The originality of this work is to propose a global approach aiming to detect and vectorize simultaneously multiple objects. The developed algorithm uses cross-section analysis to detect road curbs and vertical objects. The first results are promising, since an F-score higher than 80% has been reached, even before applying road logic rules or additional knowledge. The detection and extraction of vertical objects including facades, trees, and poles, is more challenging but the detections also present a recall greater than 85%.

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“…Deep learning technology has been used in the field of visible light image segmentation as well as far-infrared images [ 25 ]. For object detection in the nighttime, features of visible images become invalid and deep learning based on traffic vehicle detection is used to fuse data obtained from multi-sensors [ 26 ]. The majority of methods-based neural networks are only using visual sensors to improve the accuracy of target detection, but they generally do not function well in harsh conditions [ 27 ].…”

Section: The Related Workmentioning

confidence: 99%

Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes

Tan

Zhang

et al. 2022

Sensors

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Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.

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Research on Identification of Road Features from Point Cloud Data Using Deep Learning

Cited by 11 publications

References 8 publications

3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes

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