pavement profile scanner - erpug · contents about lehmann+partner pavement profile scanner + data...

PAVEMENT PROFILE SCANNER + NEXT GENERATION IN ROAD MONITORING, MODELLING AND MAPPING

MADRID 2018

Contents

✛ ABOUT LEHMANN+PARTNER

✛ PAVEMENT PROFILE SCANNER +

✛ DATA PRODUCTS AND ANALYSIS

✛ LATEST RESEARCH AND FINDINGS

Maximilian Sesselmann ERPUG 2018 Madrid, 17. – 19.10.2018

ABOUT LEHMANN+PARTNER

About LEHMANN+PARTNER


✛ based in Germany

✛ since 2017: part of Ginger group

(France, UK, Poland, Germany, India …)

✛ main business segments:

▪ road condition assessment / condition surveys

▪ road inventory value assessment

▪ developement of technological solutions

Mobile Mapping / Mobile Laserscanning Systems

3D Asset Mapping

Clearance Profile Scanner (CPS)

3D Condition Survey

Pavement Profile Scanner (PPS +)

About LEHMANN+PARTNER


ONE DRIVE

TWO SURVEYSI.R.I.S

PAVEMENT PROFILE SCANNER +

Pavement Profile Scanner +


From PPS to PPS-Plus: a brief history ...

✛ 2012: development of the first generation of Pavement Profile Scanners

✛ cooperation with Fraunhofer IPM (Freiburg, Germany)

✛ aim: develop a LiDAR profiler that meets German law and standards

▪ precision and repeatability

▪ speed and eye-safety

▪ robustness and handy size

▪ reliability under challanging weather conditions

ERPUG 2013



Some technical specifications ...

✛ manufacturer: Fraunhofer IPM

✛ sub-mm accuracy: < 0,15 mm (@ 90 % reflection)

< 0,30 mm (@ 20 % reflection)

✛ sampling rate: 1 to 2 MHz

✛ > 900 points in transverse profile

✛ scan width on ground: > 4 m (at 3 m mounting height)

✛ laser class 1 (eye-safe)

✛ IP 67 (dust and waterproof)

✛ water-cooled/heated

✛ certified by Federal Highway Research Institute (BASt)



... and what about the „plus“ ?

✛ integration of an additional laser unit for high resolution 2D imaging

✛ 4000 detectors per line (16 detector arrays)

✛ sampling rate: 64 MHz (16 x 4 MHz)

✛ resolution @ 80 km/h and 3 m height: 1.7 mm x 1.2 mm

1.2mm

1.7mm28 mm

4.5 mm

2D

/3D

-Sc

anlin

e



Integration into several Mobile Mapping Systems

✛ I.R.I.S Systems (Germany, Switzerland, Poland)

✛ RoadSTAR (Austrian Institute of Technology)

✛ MESAS/TSD (Federal Highway Research Institute Germany)

DATA PRODUCTS AND ANALYSIS

Data products and analysis


PPS+ 3D unit: 3D point clouds

✛ measure transverse and longitudinal evenness (e.g. rutting, IRI)

✛ 3D surface analysis

✛ post-processing to surface models (e.g. OpenCRG)

PPS+ 2D unit: surface images

✛ detection of surface characteristics (e.g. cracks, patches, potholes)

✛ object detection (lane markings, inventory, …)

✛ active sensor (reduced impact of shadows)

no black box: all open data formats

(e.g. ascii, las, CRG, GeoTIFF, png, GeoJSON)

Longitudinal and transversal profiles

✛ profiles can easily be extracted from the 3D point cloud

✛ geometry and evenness analysis:

▪ longitudinal: e.g. rolling straight edge, IRI, WLP, PGR

▪ transversal: e.g. rut depth, fictional water depth, crosslope

Data products and analysis: 3D


Surface distress in 3D point clouds

✛ geometric and radiometric information



Data products and analysis: 2.5D


Surface models: GeoTIFF

✛ fully compatible with contemporary GIS / CAD

✛ GeoTIFFs based either on intensity or elevation measurement (3D unit)

✛ use for automatic / manual distress digitization, digital terrain analysis, ...

intensity elevation

Data products and analysis: 2.5D


Surface models: Curved Regular Grid (CRG)

✛ CRG model based on trajectory and elevation measurement

▪ automotive industry (e.g. tire models, vibration models)

▪ 3D evenness models (e.g. evaluated acceleration on the driver seat, dynamic axle load)



High resolution surface images

✛ high resolution images: surface characteristics up to 1mm are visible

✛ very detailed information and high dynamic range

PPS+ DATA ANALYSIS: LATEST RESEARCH

PPS+ data analysis: latest research


Automatic surface image analysis approach based on Deep Learning

✛ since 2016: development as part of the ASINVOS project

✛ cooperation with Ilmenau University of Technology (robotics and neuro computer science)

▪ latest publications: Eisenbach et al. (2017), Seichter et al. (2018)

✛ based on Free and Open-Source deep learning framework TensorFlow (known from Google apps)

✛ started with modified VGG-like CNN architecture

✛ ended up with ResNet18 architecture as best trade-off: accuracy and speed

✛ the ASINVOS system offers:

▪ huge road surface database

▪ pre-trained detectors

▪ train custom detectors



...the database: GAPs

✛ publically available benchmark dataset for researchers (train, valid, test)

✛ manually annotated road surface images from all over Germany

▪ various asphalt types and materials

▪ road types from highways to rural roads

✛ 20 classes: crack, sealed crack, pothole, patch, lanemarking,

manhole, curbstone, cobblestone, vegetation ...

✛ > 4 million images from condition surveys (certified by BASt)

▪ high resolution surface images

▪ 2.5D surface models (GeoTIFFs)

✛ condensed 80k and 600k sets generated by special approach

F1-s

core

val

idat

ion

set

example count in training set

random selectionASINVOS selection

less examplesless training time

same accuracy level



... performance

✛ evaluation on ca. 23k test examples: 0.957 (F1-score)

✛ additional evaluation using a test set of images from 5

independent recording systems

▪ different recording principles

▪ different image resolutions (0.8 mm to 2 mm)

▪ these image examples were never used in the training stage

Performance on unknown data from systems never used in training the detector:

F1-scores ranging from 0.83 to 0.94 for detecting distress

F1-scores ranging from 0.92 to 0.99 for detecting intact road surface

Very good generalization properties!



... output formats

✛ basic output format is the so-called „confidence map“ (heatmap for distress)

✛ several output formats are implemented: bounding boxes, grids, vector-contours

✛ attributes can be computed (orientation, length, area ...)



... example



... outlook: approach applied to GeoTIFFs

✛ yet relatively small database (ca. 11k examples)

✛ work in progress

✛ ResNet with input patch-size 20x20 px

✛ F1-score on validation set: 0.9770

✛ F1-score on test set: 0.9687

✛ results in GIS environment

✛ ready for further spatial analysis

Conclusion


Pavement Profile Scanner Plus ...

✛ high precision LiDAR instrument made in Germany (Fraunhofer IPM)

✛ analyze both evenness and surface characteristics with one device

✛ no black box: open formats/standards

✛ wide range of data products and applications (condition monitoring, mapping, modelling)

✛ state-of-the-art automatic data analysis based on Deep Learning


References

EISENBACH, M., STRICKER, R., SEICHTER, D., AMENDE, K., DEBES, K., SESSELMANN, M., EBERSBACH, D., STOECKERT, U. & H.-M. GROSS (2017): How to Get Pavement Distress Detection Ready forDeep Learning? A Systematic Approach. Int. Joint Conf. on Neural Networks (IJCNN), Anchorage, USA, pp 2039 – 2047, doi: 10.1109/IJCNN.2017.7966101

LUNDBERG, T., ANDRÉN, P., WAHLMAN, T., ERIKSSON, O., SJOGREN, L. & P. EKDAHL (2018): New technology for road surface measurement: Transverse profile and rut depth. Swedish National Roadand Transport Research Institute (VTI). Rapport 961A. VTI: 2015/0655-9.1.

SEICHTER, D., EISENBACH, M., STRICKER, R. & H.-M. GROSS (2018): How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort. Proc. Int. Conf. onAutomation Science and Engineering (CASE), München, Germany, pp. 63-70, IEEE 2018

SESSELMANN, M. (2018): Automatische Detektion von Substanzmerkmalen auf 3D-Fahrbahnoberflächen. AGIT – Journal für angewandte Geoinformatik. 4-2018, pp 65 – 74, Wichmann Verlag, VDEVERLAG GMBH, Berlin, doi: 10.14627/537647009.

SESSELMANN, M. & M. WIELAND (2018): A 3D approach for evaluating the structural condition of jointed plain concrete pavements in a pavement management context. 13th InternationalSymposium on Concrete Roads 19 – 22 June 2018, Berlin.

TESIS (2018): Virtual test drive on OpenDrive routes with CRG surfaces. URL: https://youtu.be/YoCjfiLO5yo?list=PLFju8IVzycO2NUoRGcoa1X15Pu530kVdh

UECKERMANN, A. & M. OESER (2015): Approaches for a 3D assessment of pavement evenness data based on 3D vehicle models. Journal of Traffic and Transportation Engineering, Volume 2, Issue 2,pp 68-80.

UECKERMANN, A. & B. STEINAUER (2008): TheWeighted Longitudinal Profile. RoadMaterials and Pavement Design. Volume 9 – No. 2/2008, pp 135 - 157.

WIELAND, M. & M. SESSELMANN (2018): Plattenspezifische 3D-Oberflächenanalyse im Kontext der rechnerischen Dimensionierung und Restsubstanzbewertung von Betonfahrbahndecken. Strasseund Autobahn, 6-2018, Kirschbaum Verlag, pp 447 – 458.

Thank you!

pavement profile scanner - erpug · contents about lehmann+partner pavement profile scanner + data...

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