Affiliation:
1. Software Competence Center Hagenberg GmbH, Softwarepark 32a, 4232 Hagenberg, Austria
2. KEBA Group AG, Reindlstraße 51, 4040 Linz, Austria
Abstract
In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
Funder
Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology
Federal Ministry for Digital and Economic Affairs
State of Upper Austria
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Cited by
3 articles.
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