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Article overview
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Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturing | | Manuela Geiß
; Raphael Wagner
; Martin Baresch
; Josef Steiner
; Michael Zwick
; | | Date: |
1 Jun 2022 | | 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 for the 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. | | Source: | arXiv, 2206.00280 | | Services: | Forum | Review | PDF | Favorites | |
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