Next Article in Journal
Multivariate Statistical Approach to Image Quality Tasks
Previous Article in Journal
A Non-Structural Representation Scheme for Articulated Shapes
Article

ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding

by 1,2
1
Computer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
2
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
Received: 24 June 2018 / Revised: 21 September 2018 / Accepted: 29 September 2018 / Published: 8 October 2018
This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer model, while the decoder network uses 16 upsampling and deconvolution units. The encoder of the network has a very flexible architecture that can be altered and trained for any size and resolution of images. The decoder network upsamples and maps the low-resolution encoder’s features. Consequently, there is a substantial reduction in the trainable parameters, as the network recycles the encoder’s pooling indices for pixel-wise classification and segmentation. The proposed model is intended to offer a simplified CNN model with less overhead and higher performance. The network is trained and tested on the famous road scenes dataset CamVid and offers outstanding outcomes in comparison to similar early approaches like FCN and VGG16 in terms of performance vs. trainable parameters. View Full-Text
Keywords: convolutional neural network (CNN); ReLU; encoder-decoder; CamVid; pooling; semantic segmentation; VGG-19; ADAS convolutional neural network (CNN); ReLU; encoder-decoder; CamVid; pooling; semantic segmentation; VGG-19; ADAS
Show Figures

Graphical abstract

MDPI and ACS Style

Yasrab, R. ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding. J. Imaging 2018, 4, 116. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4100116

AMA Style

Yasrab R. ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding. Journal of Imaging. 2018; 4(10):116. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4100116

Chicago/Turabian Style

Yasrab, Robail. 2018. "ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding" J. Imaging 4, no. 10: 116. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4100116

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop