Mobile mapping for the automated analysis of road signage and delineation

McLoughlin, Simon; Deegan, Catherine; Mulvihill, Christine; Fitzgerald, Conor; Markham, Charles H.

doi:10.1049/iet-its:20060083

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Systems that automatically evaluate the retroflectivity of traffic signs, and detect defective road studs (reflective cat’s eyes) are presented, respectively, in Chengbo and Yichang ( 21 ) and Mcloughlin et al. ( 22 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision for Rapid Updating of the Highway Asset Inventory

Strain

Wilson

Littleworth

2020

Transportation Research Record: Journal of the Transportation R

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In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The feasibility of the system is tested with assets along an 8 km section of the A27 highway on the south coast of England, UK. Survey data from a vehicle equipped with a single forward-facing camera and a GPS-enabled inertial measurement unit, aerial imagery of the highway, and the asset inventory are fused to develop the system. The camera on the vehicle is calibrated so that assets may be automatically located within the survey images. The assets are then classified by a state-of-the-art convolutional neural network. Therefore, those assets recorded correctly in the inventory and those needing further manual inspection are automatically identified. Three different asset types are considered (traffic signs, matrix signs, and reference marker posts), and overall 91% of the assets in a withheld test set are verified automatically. Thus the analyst is presented with a much smaller set of assets for which the inventory is incorrect and which require further inspection. We therefore demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Vision for Rapid Updating of the Highway Asset Inventory

Strain

Wilson

Littleworth

2020

Transportation Research Record: Journal of the Transportation R

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“…While different video formats could be handled in a GIS analysis system, it is each video-survey's spatial data semantics that cause significant difficulties. Typically, each survey has been captured for a specific spatial-oriented objective where the geographical information generated may only describe video features or events over isolated frame sequences (McLoughlin et al 2008). In general, this creates a barrier to using the video's spatial data in a GIS due to potentially diverse spatial semantics.…”

Section: Video-centred/remote-sensing Approachmentioning

confidence: 99%

Spatial video and GIS

Lewis

Fotheringham

Winstanley

2011

International Journal of Geographical Information Science

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“…only a few of raised pavement markers can be imaging, the effective edge points are too small to detect implicit lane of raised pavement markers. S. McLoughlin [6] also uses edge to detected raised pavement markers. Firstly, the borders of the road studs are found in the edge image, and then the morphological operation is used to complete the object border.…”

Section: Introductionmentioning

confidence: 99%

“…If d(x, y) is bigger than a threshold D (here D = 0.05), it is a true center. d(x, y) is computed by(6…”

mentioning

confidence: 99%

Lane recognition based on location of raised pavement markers

Liu

et al. 2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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Lane recognition plays an important role in driver assistance systems. Many Lane recognition methods have been proposed until now. But most methods are used to recognize the traditional painted lines and can't work on the road where there are only raised pavement lane markers such as reflective markers and bots dots. Lane recognition on this kind of roads is a difficult problem especially in the daytime for the raised pavement markers are poorly visible and not reflected. In this paper, a novel solution for lane recognition in this case is presented. Firstly, the dot filters and the OLS templates are proposed for location of raised pavement markers. Then a line fitting method based on Hough transform and clustering is used on the accumulation image of raised pavement markers to detect the implicit lane. The experimental results under various scenes show that the proposed method is effective and can be implemented in real-time.978-1-4577-0891-6/11/$26.00 ©2011 IEEE

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Mobile mapping for the automated analysis of road signage and delineation

Cited by 6 publications

References 13 publications

Computer Vision for Rapid Updating of the Highway Asset Inventory

Computer Vision for Rapid Updating of the Highway Asset Inventory

Spatial video and GIS

Lane recognition based on location of raised pavement markers

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