Title :

Railway Obstacle Detection Using Unsupervised Learning: An Exploratory Study

Author(s) :

Amine, Boussik [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Ben Messaoud, Wael [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Niar, Smail [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Taleb-Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

32nd IEEE Intelligent Vehicles Symposium (IV'21)

City :

Nagoya

Country :

Japon

Start date of the conference :

2021-07-11

Journal title :

2021 IEEE Intelligent Vehicles Symposium (IV)

Publisher :

IEEE

English keyword(s) :

Training
Rails
Measurement
Decision making
Rail transportation
Real-time systems
Unsupervised learning

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

Autonomous Driving (AD) systems are heavily reliant on supervised models. In these approaches, a model is trained to detect only a predefined number of obstacles. However, for applications like railway obstacle detection, ...
Show more >Autonomous Driving (AD) systems are heavily reliant on supervised models. In these approaches, a model is trained to detect only a predefined number of obstacles. However, for applications like railway obstacle detection, the training dataset is limited and not all possible obstacle classes are known beforehand. For such safety-critical applications, this situation is problematic and could limit the performance of obstacle detection in autonomous trains. In this paper, we propose an exploratory study using unsupervised models based on a large set of generated convolutional autoencoder models to detect obstacles on railway's track level. The study was conducted based on three components: loss functions, activations and optimizers. Existing works rely on fixing thresholds to judge the performance of the model. We propose instead a methodology based on Multi-Criteria Decision Making (MCDM) to evaluate the performance of all models. Furthermore, we introduce the notion of gap-score to evaluate each model by calculating the average difference between the reconstruction score on images with and without obstacles. The aim is to find models maximizing the average of gap-scores and rank them according to their performances. Experimental results show that the evaluated models can provide up to 68 % average gap-scoreShow less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL