Road sign detection and localization based on camera and lidar data

Buyval, Alexander; Gabdullin, Aidar; Lyubimov, Maxim

doi:10.1117/12.2523155

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…Velodyne's VLP-16 sensor is the smallest advanced sensor in Velodyne's 3D LiDAR product range. Buyval et al [62] proposed a method on board an autonomous vehicle for road sign detection and localization using the VLP-16 sensor. The researchers used this twenty-fourth platform reviewed to implement their algorithm for road sign s classification and localization in a 3D space.…”

Section: Mobile Data Acquisition Systemsmentioning

confidence: 99%

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Using mobile laser scanner and imagery for urban management applications

González-Barbosa

Flores-Rodrıguez

Ornelas-Rodríguez

et al. 2022

IJRA

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<p>Despite autonomous navigation is one of the most proliferate applications of three-dimensional (3D) point clouds and imagery both techniques can potentially have many other applications. This work explores urban digitization tools applied to 3D geometry to perform urban tasks. We focus exclusively on compiling scientific research that merges mobile laser scanning (MLS) and imagery from vision systems. The major contribution of this review is to show the evolution of MLS combined with imagery in urban applications. We review systems used by public and private organizations to handle urban tasks such as historic preservation, roadside assistance, road infrastructure inventory, and public space study. The work pinpoints the potential and accuracy of data acquisition systems to handled both 3D point clouds and imagery data. We highlight potential future work regarding the detection of urban environment elements and to solve urban problems. This article concludes by discussing the major constraints and struggles of current systems that use MLS combined with imagery to perform urban tasks and to solve urban tasks.</p>

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Section: Mobile Data Acquisition Systemsmentioning

confidence: 99%

“…In this sense, semantic detection has simplified the traffic sign detection task. 3D information is reprojected onto 2D images, and D-ANNs are used for sign classification, as discussed in [47], [39], [61], [62], [86]- [88]. These systems use CNNs hierarchically organized based on supervised learning and contain several specialized hidden layers.…”

mentioning

confidence: 99%

Using mobile laser scanner and imagery for urban management applications

González-Barbosa

Flores-Rodrıguez

Ornelas-Rodríguez

et al. 2022

IJRA

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“…A dataset was collected to achieve this goal and train the neural network (built on the Faster-R-CNN architecture). The device generates a series of images with bounding boxes and points clouds related to actual road signs [14]. The first section of a method for detecting and classifying road signs identifies the road signs on a real-time basis.…”

Section: Related Workmentioning

confidence: 99%

Road Sign Detection Using Variants of YOLO and R-CNN: An Analysis from the Perspective of Bangladesh

Lima

Kabir

Das

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Road sign detection represents a feature that assures the safety of drivers, vehicles, and pedestrians by efficiently detecting road signs. This feature is designed to notify drivers about road signs whether he is missing the signs or not. This detecting and recognizing feature of the road signs' has improved a part of the advanced driver assistance system (ADAS). ADAS is an automated technology containing cameras and sensors intended to help the drivers with road signs, while traveling to a new road or having no knowledge about road signs. Before the work analysis, this topic has shown formidability as it has a real-time processing solution. This paper analyzed seven architectures for detecting the road signs: YOLO, YOLOv2, YOLOv3, PP-YOLO model and R-CNN, Fast R-CNN, Faster R-CNN. We have built a dataset based on Bangladesh's road sign named the "BD Road Sign 2021 (BDRS 2021)" dataset to evaluate the architectures. This dataset contains 16 categories (16 types of road-sign), and each has 168 images. Finally, we applied the seven advanced architectures to find the effective one to detect Bangladesh's road signs. This study implies that YOLOv3 and Faster R-CNN perform comparatively better for road sign detection.

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“…Использование камер является более традиционным методом, который имитирует зрение водителя. В области компьютерного зрения распознавание объектов является одной из основных задач, для решения которой используют как стандартные алгоритмы компьютерного зрения [4], так и использования различных архитектур нейронных систем [1]. Метод, описанный в данной статье, основан на использовании полносверточной нейронной сети [6], выполняющей задачу сегментации изображения.…”

Section: Introductionunclassified

Object Recognition of Road Infrastructure Using a Fully Convolutional Neural Network

Azarchenkov

Lyubimov

Lushkov

2019

Automation and Modeling in Design and Management

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This article presents a method for recognizing key objects of the road infrastructure using a fully convolutional neural network. The result of the neural network is a segmented image, where the desired objects are highlighted in certain colors. At the post-processing stage, a section of the roadway along which the car moves is selected, as well as the calculation of the parameters of the bounding rectangles for each of the objects. This method allows you to localize the road, pedestrian crossing, cars, traffic signs, traffic lights, pedestrians. Testing of the developed algorithm was carried out on a model of the urban infrastructure at a scale of 1:18, where a wheeled robot acted as a car.

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Road sign detection and localization based on camera and lidar data

Cited by 8 publications

References 6 publications

Using mobile laser scanner and imagery for urban management applications

Using mobile laser scanner and imagery for urban management applications

Road Sign Detection Using Variants of YOLO and R-CNN: An Analysis from the Perspective of Bangladesh

Object Recognition of Road Infrastructure Using a Fully Convolutional Neural Network

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