Publication detail

Transfer Learning for Deep Convolutional Neural Network from RGB to IR Domain

LIGOCKI, A. JELÍNEK, A.

Original Title

Transfer Learning for Deep Convolutional Neural Network from RGB to IR Domain

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

In this paper, we are presenting a proof of concept of our system for training of the YOLOv3 neural network for object detection of vehicles in thermal camera images. Our approach is unique in the way we are using a dataset containing a large number of synchronized range measurements as well as RGB and thermal images. We are using the existing YOLO toolkit to detect objects on the RGB images, we estimate detection distance by the LiDAR and later we reproject these detections into the IR image. In this way, we have created a large dataset of annotated thermal images that helped us to significantly improve the performance of the neural network at the IR domain.

Keywords

Neural Networks, IR Camera, Object Detection, RGB to IR, Thermal imaging, YOLO, Transfer Learning

Authors

LIGOCKI, A.; JELÍNEK, A.

Released

24. 4. 2020

Pages count

5

BibTex

@inproceedings{BUT163717,
  author="Adam {Ligocki} and Aleš {Jelínek}",
  title="Transfer Learning for Deep Convolutional Neural Network from RGB to IR Domain",
  year="2020",
  pages="5"
}