Vývoj a zhodnocení konceptu pro rozšíření dat pro trénování neuronových sítí pro semantickou segmentaci LiDAR-Pointclouds
Development and Evaluation of a Concept for the Augmentation of Data to Train Neuronal Networks for Semantic Segmentation of LiDAR-Pointclouds
Type of document
diplomová prácemaster thesis
Author
Till Schöpe
Supervisor
Lampe Bastian
Opponent
Svoboda Tomáš
Field of study
Kybernetika a robotikaStudy program
Kybernetika a robotikaInstitutions assigning rank
katedra řídicí technikyRights
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
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An accurate environment perception is a key requirement for automated vehicles. To help ful-fill this requirement, a promising approach is the semantic segmentation of LiDAR-pointcloudsusing neural networks. The acquisition of large amounts of annotated data needed for trainingthese networks is challenging due to the high cost of manually labeling pointclouds.In this work, data augmentation is proposed to obtain a high number of annotated LiDAR-pointclouds without the need of manual labeling. Two augmentation strategies are presented,the first one being the creation of semi-artificial samples and the second one the application oflabel-preserving transformations. Semi-artificial samples are created by automatically extract-ing objects from a controlled environment and inserting them into scenes where these objectsdo not occur.The results suggest that semi-artificial pointclouds can be successfully used as a supplementor as a substitution of real data. An intersection over union of 31.7 % can be achieved on a dataset of real world scenes when only semi-artificial pointclouds are used for training. Additionally,the recall on the real world data set can be increased if the semi-artificial data set is furtheraugmented with label-preserving transformations. An accurate environment perception is a key requirement for automated vehicles. To help ful-fill this requirement, a promising approach is the semantic segmentation of LiDAR-pointcloudsusing neural networks. The acquisition of large amounts of annotated data needed for trainingthese networks is challenging due to the high cost of manually labeling pointclouds.In this work, data augmentation is proposed to obtain a high number of annotated LiDAR-pointclouds without the need of manual labeling. Two augmentation strategies are presented,the first one being the creation of semi-artificial samples and the second one the application oflabel-preserving transformations. Semi-artificial samples are created by automatically extract-ing objects from a controlled environment and inserting them into scenes where these objectsdo not occur.The results suggest that semi-artificial pointclouds can be successfully used as a supplementor as a substitution of real data. An intersection over union of 31.7 % can be achieved on a dataset of real world scenes when only semi-artificial pointclouds are used for training. Additionally,the recall on the real world data set can be increased if the semi-artificial data set is furtheraugmented with label-preserving transformations.
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