loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Sara Khan 1 ; Mehmed Yüksel 2 and Andre Ferreira 3

Affiliations: 1 Robert Bosch GmbH, Renningen, Germany ; 2 DFKI GmbH Robotics Innovation Center, Bremen, Germany ; 3 Robert Bosch Car Multimedia S.A., Braga, Portugal

Keyword(s): Automobile, Machine Learning, Damage detection, Cosmetic Damages, Inertial Sensors, Autoencoders.

Abstract: Every vehicle is susceptible to several types of small physical damage such as dents and scratches. These damages can be seen as cosmetic damages as they impact the vehicle’s visual and value but do not alter its main functions. Vehicle owners, insurance companies, and the car-rental/taxi-service companies are especially keen to detect the events that generate these kinds of damages. The ability to detect impact events is valuable to monitor the occurrence of possible damages to the vehicles. In this paper, we present a novel acceleration spectrogram-based Machine Learning (ML) approach for dynamic (real-time) small vehicle damage detection using inertial sensors. Inertial sensors are low-resource consumption sensors, which makes the proposed solution economical. Conventionally, inertial sensors are used in the airbag control system but they are not developed to detect impacts that generate minor damages. Most of the previous work on small impact detection either uses smartphone iner tial data which is not accurate or focuses on static damage detection based on image sensory inputs. Our intelligent impact and damage detection ML-based system uses autoencoders as an automatic feature extractor using acceleration spectrograms and classifies the sensory encoded feature representation into damage or non-damage. It can achieve an accuracy of 0.8. This approach sets the stage for various potential research directions in damage detection. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.28.0.143

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Khan, S.; Yüksel, M. and Ferreira, A. (2023). ASIMS: Acceleration Spectrograms Based Intelligent Mobility System for Vehicle Damage Detection. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 179-186. DOI: 10.5220/0011763200003479

@conference{vehits23,
author={Sara Khan. and Mehmed Yüksel. and Andre Ferreira.},
title={ASIMS: Acceleration Spectrograms Based Intelligent Mobility System for Vehicle Damage Detection},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={179-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011763200003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - ASIMS: Acceleration Spectrograms Based Intelligent Mobility System for Vehicle Damage Detection
SN - 978-989-758-652-1
IS - 2184-495X
AU - Khan, S.
AU - Yüksel, M.
AU - Ferreira, A.
PY - 2023
SP - 179
EP - 186
DO - 10.5220/0011763200003479
PB - SciTePress