scholarly journals Photogrammetric 3D mobile mapping of rail tracks

2022 ◽  
Vol 183 ◽  
pp. 352-362
Author(s):  
P. Glira ◽  
K. Ölsböck ◽  
T. Kadiofsky ◽  
M. Schörghuber ◽  
J. Weichselbaum ◽  
...  
Keyword(s):  
Mobile Mapping

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

A Mobile Mapping System for road data capture based on 3D road model

2006 ◽  
Author(s):  
Kiichiro Ishikawa ◽  
Jun-ichi Takiguchi ◽  
Yoshiharu Amano ◽  
Takumi Hashizume
Keyword(s):  
Data Capture ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Road Data

scholarly journals Mobile Mapping of Sporting Event Spectators Using Bluetooth Sensors: Tour of Flanders 2011

Sensors ◽  
2012 ◽  
Vol 12 (10) ◽  
pp. 14196-14213 ◽  
Author(s):  
Mathias Versichele ◽  
Tijs Neutens ◽  
Stephanie Goudeseune ◽  
Frederik van Bossche ◽  
Nico Van de Weghe
Keyword(s):  
Mobile Mapping ◽  
Sporting Event

Stereopolis II: A multi-purpose and multi-sensor 3D mobile mapping system for street visualisation and 3D metrology

2014 ◽  
pp. 69-79
Author(s):  
Nicolas Paparoditis ◽  
Jean-Pierre Papelard ◽  
Bertrand Cannelle ◽  
Alexandre Devaux ◽  
Bahman Soheilian ◽  
...  
Keyword(s):  
Software As A Service ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Grand Public ◽  
3D Metrology

Nous présentons dans cet article un système de numérisation mobile 3D hybride laser-image qui permet d'acquérir des infrastructures de données spatiales répondant aux besoins d'applications diverses allant de navigations multimédia immersives jusqu'à de la métrologie 3D à travers le web. Nous détaillons la conception du système, ses capteurs, son architecture et sa calibration, ainsi qu'un service web offrant la possibilité de saisir en 3D via un outil de type SaaS (Software as a Service), permettant à tout un chacun d'enrichir ses propres bases de données à hauteur de ses besoins.Nous abordons également l'anonymisation des données, à savoir la détection et le floutage de plaques d'immatriculation, qui est est une étape inévitable pour la diffusion de ces données sur Internet via des applications grand public.

Sign in / Sign up

Export Citation Format

Share Document