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J. Imaging 2018, 4(10), 116; https://doi.org/10.3390/jimaging4100116

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

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
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Abstract

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yasrab, R. ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding. J. Imaging 2018, 4, 116.

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