Year: 2024
Type: article
Abstract: Object detection often struggles with accurately identifying visually similar parts, a challenge commonly faced in industrial applications. To address this issue, we propose a clustering methodology based on the visual similarity of 3D object models. This approach is particularly effective when integrated with synthetic image generation, as both processes rely on 3D models. In a case study, we observed a 22 % increase in classification performance on a validation dataset when training an object detector on visually similar groups rather than on all classes, suggesting the potential of our method as a baseline for a multi-stage classification scheme.
Source: Information
DOI: 10.3390/info15120761
Authors: Julian Rolf, Detlef Gerhard, Pero Kosic
Institution: Ruhr University Bochum
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
OpenAlex ID: W4404959057