Publication detail

Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data

LIGOCKI, A. JELÍNEK, A. ŽALUD, L. RAHTU, E.

Original Title

Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data

Type

journal article in Web of Science

Language

English

Original Abstract

One of the biggest challenges of training deep neural network is the need for massive data annotation. To train the neural network for object detection, millions of annotated training images are required. However, currently, there are no large-scale thermal image datasets that could be used to train the state of the art neural networks, while voluminous RGB image datasets are available. This paper presents a method that allows to create hundreds of thousands of annotated thermal images using the RGB pre-trained object detector. A dataset created in this way can be used to train object detectors with improved performance. The main gain of this work is the novel method for fully automatic thermal image labeling. The proposed system uses the RGB camera, thermal camera, 3D LiDAR, and the pre-trained neural network that detects objects in the RGB domain. Using this setup, it is possible to run the fully automated process that annotates the thermal images and creates the automatically annotated thermal training dataset. As the result, we created a dataset containing hundreds of thousands of annotated objects. This approach allows to train deep learning models with similar performance as the common human-annotation-based methods do. This paper also proposes several improvements to fine-tune the results with minimal human intervention. Finally, the evaluation of the proposed solution shows that the method gives significantly better results than training the neural network with standard small-scale hand-annotated thermal image datasets.

Keywords

deep convolutional neural networks; transfer learning; YOLO; RGB; IR; thermal; data annotation; object detector

Authors

LIGOCKI, A.; JELÍNEK, A.; ŽALUD, L.; RAHTU, E.

Released

24. 2. 2021

Publisher

MDPI

Location

Postfach, CH-4020 Basel, Switzerland

ISBN

1424-8220

Periodical

SENSORS

Year of study

21

Number

4

State

Swiss Confederation

Pages from

1

Pages to

23

Pages count

23

URL

https://www.mdpi.com/1424-8220/21/4/1552

Full text in the Digital Library

http://hdl.handle.net/11012/196455

BibTex

@article{BUT170007,
  author="Adam {Ligocki} and Aleš {Jelínek} and Luděk {Žalud} and Esa {Rahtu}",
  title="Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data",
  journal="SENSORS",
  year="2021",
  volume="21",
  number="4",
  pages="1--23",
  doi="10.3390/s21041552",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/21/4/1552"
}