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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU136318" target="_blank" >RIV/00216305:26220/20:PU136318 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Transfer Learning for Deep Convolutional Neural Network from RGB to IR Domain
Original language description
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.
Czech name
—
Czech description
—

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)