Year: 2024
Type: article
Abstract: Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer’s images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals.
Source: Applied Sciences
DOI: 10.3390/app14209560
Institution: Universidad de Sevilla
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Funders: Nvidia, Agencia Estatal de Investigación
OpenAlex ID: W4403588039