@article{bystrov20252766,
Author = {Aleksandr Bystrov and Ole Meyer and Fabian Kott and Karin Saemann and Achim Maat and Lisa Dawel},
Title = {Sustainable preprocessing for AI repairability assessment},

Journal = {IFAC-PapersOnLine},
Year = {2025},
Pages = {2766-2771},
Month = {},
Doi = {https://doi.org/10.1016/j.ifacol.2025.09.465},
type = {article},
Abstract = {The automotive industry increasingly embraces repair practices driven by sustainability trends shaped by government policies and consumer preferences. Artificial intelligence (AI) enhances repair processes through versatile applications like computer vision. However, its energy demands and carbon footprint have recently garnered attention. Image data preprocessing techniques applied during the data derivation stage offer solutions to reduce AI’s environmental impact. This paper presents novel preprocessing strategies implemented during the data collection phase to address challenges in applying computer vision within the automotive repair industry. The study balances the trade-of between deployment benefits and energy demands, particularly focusing on Fault Detection Models.}
}
@COMMENT{Bibtex file generated on }