Clustering Visual Similar Objects for Enhanced Synthetic Image Data for Object Detection

Julian Rolf; Detlef Gerhard; Pero Kosic

doi:10.20944/preprints202408.1793.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 23 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (07:09:12 CEST)

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.

similarity analysis; object detection; deep embedded clustering

Engineering, Industrial and Manufacturing Engineering

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Clustering Visual Similar Objects for Enhanced Synthetic Image Data for Object Detection

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