Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory

Soilán, Mario; Riveiro, Belén; Martínez-Sánchez, J.; Arias, Pedro

doi:10.1016/j.isprsjprs.2016.01.019

Cited by 88 publications

(84 citation statements)

References 23 publications

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“…Given the high contrast in the reflectance and absorbance properties between the metallic panel frames and the surface of the PV cells, the intensity attribute on the visible spectrum is used for the classification of frame/non-frame points. Assuming that the intensity distribution of reflective and non-reflective points follows a normal distribution, they can be fitted to a Gaussian Mixture Model (GMM) with two components [58]. Thus, the first component is assigned to a Gaussian distribution centered on a high intensity value (high-reflective points), while the second component is associated to the Gaussian distribution centered on a low intensity value, for non-reflective points.…”

Section: Intensity-based Segmentation Of Pv Panelsmentioning

confidence: 99%

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

et al. 2017

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A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m 2 for the area of each solar panel.

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Section: Intensity-based Segmentation Of Pv Panelsmentioning

confidence: 99%

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

et al. 2017

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“…Both shape features of pole-like objects and their surrounding pole-like objects distributions are used in this method. (Yang et al, 2013;Huang and You, 2015;Soilán et al, 2016;Lehtomäki et al, 2016) employ SVM in combination with defined features to classify point clouds of urban scene by using SVM. Random forest is adopted with manually drafted features to identify objects from MLS data by (Fukano et al, 2015;Hackel et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Semantic Labelling of Road Furniture in Mobile Laser Scanning Data

Elberink

Vosselman

2017

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

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ABSTRACT:Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. Much research has been investigated on road furniture interpretation in both 2D images and 3D point clouds. Precise interpretation of road furniture in mobile laser scanning data still remains unexplored. In this paper, a novel method is proposed to interpret road furniture based on their logical relations and functionalities. Our work represents the most detailed interpretation of road furniture in mobile laser scanning data. 93.3% of poles are correctly extracted and all of them are correctly recognised. 94.3% of street light heads are detected and 76.9% of them are correctly identified. Despite errors arising from the recognition of other components, our framework provides a promising solution to automatically map road furniture at a detailed level in urban environments.

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“…Several scholars have put effort into road furniture recognition by introducing supervised approaches [18,[30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

Elberink

Vosselman

2018

Remote Sensing

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Abstract:Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture-such as the pillars of buildings-will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation.

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Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory

Cited by 88 publications

References 23 publications

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

Semantic Labelling of Road Furniture in Mobile Laser Scanning Data

Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

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