Object Segmentation for Autonomous Driving Using iseAuto Data

Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in...

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Vydané v:Electronics (Basel) Ročník 11; číslo 7; s. 1119
Hlavní autori: Gu, Junyi, Bellone, Mauro, Sell, Raivo, Lind, Artjom
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.04.2022
Predmet:
Algorithms
Artificial neural networks
Automobiles
Autonomous vehicles
Cameras
Clouds
Data acquisition
Datasets
Deep learning
In vehicle
Labeling
Lidar
Machine learning
Neural networks
Night
Segmentation
Semantics
Semi-supervised learning
Sensors
Weather
ISSN:2079-9292, 2079-9292
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Shrnutí:Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy weather. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11071119