Authors:
Sandra Dixe
;
João Leite
;
Sahar Azadi
;
Pedro Faria
;
José Mendes
;
Jaime C. Fonseca
and
João Borges
Affiliation:
Algoritmi Center, University of Minho, Guimarães, Portugal
Keyword(s):
Semantic Segmentation, Shared Autonomous Vehicles, Deep Learning, Supervised Learning.
Abstract:
Shared autonomous vehicles (SAV) numbers are going to increase over the next years. The absence of human
driver will create a new paradigm for in-car safety. This paper addresses the problem, presenting a monitoring
system capable of estimating the state of the car interior, namely the presence of damage, dirt and stains. We
propose the use of Semantic Segmentation methods to perform appropriate pixel-wise classification of certain
textures found in the car’s cabin as defect classes. Two methods, U-Net and DeepLabV3+, were trained and
tested for different hiper-parameter and ablation scenarios, using RGB images. To be able to test and validate
these approaches an In-car dataset was created, comprised by 1861 samples from 78 cars, and than splitted in
1303 train, 186 validation and 372 test RGB images. DeepLabV3+ showed promissing results, achieving an
average accuracy for good, damage, stain and dirt of 77.17%, 58.60%, 65.81% and 68.82%, respectively.