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Appl. Sci. 2017, 7(4), 312; doi:10.3390/app7040312

An Encoder-Decoder Based Convolution Neural Network (CNN) for Future Advanced Driver Assistance System (ADAS)

Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
Author to whom correspondence should be addressed.
Academic Editor: Christos Bouras
Received: 23 January 2017 / Revised: 28 February 2017 / Accepted: 17 March 2017 / Published: 23 March 2017
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We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict pixel-wise class labels of a given input image. Recently, scene understanding has turned out to be one of the emerging areas of research, and pixel-wise semantic segmentation is a key tool for visual scene understanding. Among future intelligent systems, the Advanced Driver Assistance System (ADAS) is one of the most favorite research topic. The CSSA is a road scene understanding CNN that could be a useful constituent of the ADAS toolkit. The proposed CNN network is an encoder-decoder model, which is built on convolutional encoder layers adopted from the Visual Geometry Group’s VGG-16 net, whereas the decoder is inspired by segmentation network (SegNet). The proposed architecture mitigates the limitations of the existing methods based on state-of-the-art encoder-decoder design. The encoder performs convolution, while the decoder is responsible for deconvolution and un-pooling/up-sampling to predict pixel-wise class labels. The key idea is to apply the up-sampling decoder network, which maps the low-resolution encoder feature maps. This architecture substantially reduces the number of trainable parameters and reuses the encoder’s pooling indices to up-sample to map pixel-wise classification and segmentation. We have experimented with different activation functions, pooling methods, dropout units and architectures to design an efficient CNN architecture. The proposed network offers a significant improvement in performance in segmentation results while reducing the number of trainable parameters. Moreover, there is a considerable improvement in performance in comparison to the benchmark results over PASCAL VOC-12 and the CamVid. View Full-Text
Keywords: Convolutional Neural Network (CNN); ADAS; PReLU; Maxout; Caffe; pooling; upsampling; dropout Convolutional Neural Network (CNN); ADAS; PReLU; Maxout; Caffe; pooling; upsampling; dropout

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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.; Gu, N.; Zhang, X. An Encoder-Decoder Based Convolution Neural Network (CNN) for Future Advanced Driver Assistance System (ADAS). Appl. Sci. 2017, 7, 312.

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