Advanced Driving Assistance Based on the Fusion of Infrared and Visible Images.
Entropy (Basel)
; 23(2)2021 Feb 19.
Article
em En
| MEDLINE
| ID: mdl-33669599
ABSTRACT
Obtaining key and rich visual information under sophisticated road conditions is one of the key requirements for advanced driving assistance. In this paper, a newfangled end-to-end model is proposed for advanced driving assistance based on the fusion of infrared and visible images, termed as FusionADA. In our model, we are committed to extracting and fusing the optimal texture details and salient thermal targets from the source images. To achieve this goal, our model constitutes an adversarial framework between the generator and the discriminator. Specifically, the generator aims to generate a fused image with basic intensity information together with the optimal texture details from source images, while the discriminator aims to force the fused image to restore the salient thermal targets from the source infrared image. In addition, our FusionADA is a fully end-to-end model, solving the issues of manually designing complicated activity level measurements and fusion rules existing in traditional methods. Qualitative and quantitative experiments on publicly available datasets RoadScene and TNO demonstrate the superiority of our FusionADA over the state-of-the-art approaches.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Qualitative_research
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article