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Convolution-deconvolution architecture with the pyramid pooling module for semantic segmentation

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Abstract

Recognizing the content of an image is an important challenge in machine vision. Semantic segmentation is one of the most important ways to overcome this challenge. It is utilized in different applications such as autonomous driving, indoor navigation, virtual or augmented reality systems, and recognition tasks. In this paper, a novel and practical deep fully convolutional neural network architecture was introduced for semantic pixel-wise segmentation termed as P-DecovNet. The proposed architecture combines the Convolution-Deconvolution Neural Network architecture with the Pyramid Pooling Module. In this project, the high-level features were extracted from the image using the Convolutional Neural Network. To reinforce the local information, the Pooling module was added to the architecture. CamVid road scene dataset was used to evaluate the performance of the P-DecovNet. With respect to different criteria (including - but not limited to - accuracy and mIoU), the experimental results demonstrated that P-DecovNet practically has a good performance in the domain of Convolution-Deconvolution Network. To achieve such performance, this work uses a smaller number of training images with lesser iterations compared to the existing methods.

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Notes

  1. https://github.com/malekijoo/P-DecovNet.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Semnan University, Semnan, Iran

    Amirhossein Malekijoo & Mohammad Javad Fadaeieslam

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  1. Amirhossein Malekijoo
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  2. Mohammad Javad Fadaeieslam
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Correspondence to Amirhossein Malekijoo.

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Malekijoo, A., Fadaeieslam, M.J. Convolution-deconvolution architecture with the pyramid pooling module for semantic segmentation. Multimed Tools Appl 78, 32379–32392 (2019). https://doi.org/10.1007/s11042-019-07990-7

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