Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturing

TL;DR

This paper explores how to automatically generate bounding box annotations for object detection in industrial manufacturing using small training datasets, focusing on homogeneous backgrounds and human-provided labels.

Contribution

It adapts and compares Faster R-CNN and Scaled Yolov4-p5 for automatic annotation with minimal training data in industrial settings.

Findings

Both models can distinguish objects from homogeneous backgrounds with limited data.

Adapted models achieve promising accuracy in automatic bounding box generation.

The approach facilitates rapid model adaptation in industrial environments.

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.