Road detection using convolutional neural networks

2017 ◽  
Author(s):  
Aparajit Narayan ◽  
Elio Tuci ◽  
Frederic Labrosse ◽  
Muhanad H. Mohammed Alkilabi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Road Detection

scholarly journals LIDAR–camera fusion for road detection using fully convolutional neural networks

2019 ◽  
Vol 111 ◽  
pp. 125-131 ◽  
Author(s):  
Luca Caltagirone ◽  
Mauro Bellone ◽  
Lennart Svensson ◽  
Mattias Wahde
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Road Detection ◽  
Fully Convolutional Neural Networks

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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