PAVEMENT PROFILE SCANNER +

Transcription

1 PAVEMENT PROFILE SCANNER + NEXT GENERATION IN ROAD MONITORING, MODELLING AND MAPPING MADRID 2018

2 Contents ABOUT LEHMANN+PARTNER PAVEMENT PROFILE SCANNER + DATA PRODUCTS AND ANALYSIS LATEST RESEARCH AND FINDINGS

3 ABOUT LEHMANN+PARTNER

4 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

5 About LEHMANN+PARTNER I.R.I.S ONE DRIVE TWO SURVEYS 3D Asset Mapping Clearance Profile Scanner (CPS) 3D Condition Survey Pavement Profile Scanner (PPS +)

6 PAVEMENT PROFILE SCANNER +

7 Pavement Profile Scanner + From PPS to PPS-Plus: a brief history : 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

8 Pavement Profile Scanner + 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)

9 2D/3D- Scanline Pavement Profile Scanner +... 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) 80 km/h and 3 m height: 1.7 mm x 1.2 mm 28 mm 1.7mm 4.5 mm 1.2mm

10 Pavement Profile Scanner + 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)

11 DATA PRODUCTS AND ANALYSIS

12 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)

13 Data products and analysis: 3D 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 Maximilian Sesselmann ERPUG 2018 Madrid,

14 Data products and analysis: 3D Surface distress in 3D point clouds geometric and radiometric information Maximilian Sesselmann ERPUG 2018 Madrid,

15 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

16 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)

17 Data products and analysis: 2D High resolution surface images high resolution images: surface characteristics up to 1mm are visible very detailed information and high dynamic range

18 Data products and analysis: 2D High resolution surface images high resolution images: surface characteristics up to 1mm are visible very detailed information and high dynamic range

19 PPS+ DATA ANALYSIS: LATEST RESEARCH

20 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

21 PPS+ data analysis: latest research F1-score validation set...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 less examples less training time same accuracy level random selection ASINVOS selection example count in training set

22 PPS+ data analysis: latest research... performance evaluation on ca. 23k test examples: (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!

23 PPS+ data analysis: latest research... 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...)

24 PPS+ data analysis: latest research... example

25 PPS+ data analysis: latest research... 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: F1-score on test set: results in GIS environment ready for further spatial analysis

26 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

27 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 for Deep Learning? A Systematic Approach. Int. Joint Conf. on Neural Networks (IJCNN), Anchorage, USA, pp , doi: /IJCNN LUNDBERG, T., ANDRÉN, P., WAHLMAN, T., ERIKSSON, O., SJO GREN, L. & P. EKDAHL (2018): New technology for road surface measurement: Transverse profile and rut depth. Swedish National Road and Transport Research Institute (VTI). Rapport 961A. VTI: 2015/ 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. on Automation Science and Engineering (CASE), München, Germany, pp , IEEE 2018 SESSELMANN, M. (2018): Automatische Detektion von Substanzmerkmalen auf 3D-Fahrbahnoberflächen. AGIT Journal für angewandte Geoinformatik , pp 65 74, Wichmann Verlag, VDE VERLAG GMBH, Berlin, doi: / SESSELMANN, M. & M. WIELAND (2018): A 3D approach for evaluating the structural condition of jointed plain concrete pavements in a pavement management context. 13th International Symposium on Concrete Roads June 2018, Berlin. TESIS (2018): Virtual test drive on OpenDrive routes with CRG surfaces. URL: UECKERMANN, A. & M. OESER (2015): Approaches for a 3D assessment ofpavement evenness data based on 3D vehicle models. Journal of Traffic and Transportation Engineering, Volume 2, Issue 2, pp UECKERMANN, A. & B. STEINAUER (2008): The Weighted Longitudinal Profile. Road Materials and Pavement Design. Volume 9 No. 2/2008, pp WIELAND, M. & M. SESSELMANN (2018): Plattenspezifische 3D-Oberflächenanalyse im Kontext der rechnerischen Dimensionierung und Restsubstanzbewertung von Betonfahrbahndecken. Strasse und Autobahn, , Kirschbaum Verlag, pp

28 Thank you!