Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Wen, Chenglu; Li, Jonathan; Luo, Haipeng; Yu, Yongtao; Cai, Zhipeng; Wang, Hanyun; Wang, Cheng

doi:10.1109/tits.2015.2418214

Cited by 56 publications

(44 citation statements)

References 41 publications

(42 reference statements)

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“…The modifications in the methodology have significantly improved the recall although the precision is slightly worse. Finally, a comparison with Wen et al (2015) shows a better global performance of this method. However, the case studies are different for both results, therefore the comparison may not be totally accurate.…”

Section: Resultsmentioning

confidence: 89%

“…However, the resolution of a point cloud is not enough to distinguish the specific meaning of a traffic sign, therefore the study of optical images is needed. Wen et al (2015) detect traffic signs based on their retroreflectivity, and project the 3D data on 2D images in order to classify the previously detected traffic signs. There exists a vast literature regarding traffic sign recognition in RGB images.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, the proposed method is detailed. In Section 3, the study case and the obtained results are shown, and a comparison with Riveiro et al (2015) and with Wen et al (2015) method (which follows a similar workflow) is established. Finally, the conclusions are presented in Section 4.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

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ABSTRACT:The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

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Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

View full text Add to dashboard Cite

show abstract

“…In [32], a processing chain of retro-intensity filtering, elevation filtering, lateral offset filtering, point regrouping, and hit count filtering was developed to detect traffic signposts. In [33], first, a point cloud was segmented into isolated objects. Then, eigenvalue analysis was performed to extract objects with linear structures.…”

Section: B Traffic Signpost Detectionmentioning

confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

Self Cite

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This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportationrelated applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.

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“…As a crucial component in the driver assistance system, effective road traffic sign detection method has attracted more attention. However, as it is difficult to analyse the road traffic environment, a lot of obstacles should be handled carefully (Wen et al, 2016;Liu et al, 2016;Gregory et al, 2016). To enhance city traffic management level, intelligent transportation system (ITS) is proposed and developed.…”

Section: Introductionmentioning

confidence: 99%

Road traffic sign recognition algorithm based on computer vision

Dai

Zhang

Yang

2018

IJCVR

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As road traffic sign recognition is a crucial component for automatic driver assistance systems, it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilising the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to grey scale images, and then grey scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilised to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN-based traffic sign recognition performs better than the traditional CNN.

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Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Cited by 56 publications

References 41 publications

Automatic Road Sign Inventory Using Mobile Mapping Systems

Automatic Road Sign Inventory Using Mobile Mapping Systems

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Road traffic sign recognition algorithm based on computer vision

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