ASIMS: Acceleration Spectrograms Based Intelligent Mobility System for Vehicle Damage Detection

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 inertial 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.

Keywords :

Automobile, Machine Learning, Damage detection, Cosmetic Damages, Inertial Sensors, Autoencoders

Links:

https://www.scitepress.org/Link.aspx?doi=10.5220/0011763200003479