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Scan-Net: A Sparsely Encoded Convolutional Autoencoder for Semantic Segmentation of Unknown Terrain Dept. of ECE, University of Alabama at Birmingham, Birmingha
- Format:
- Book
- Conference/Event
- Author/Creator:
- Haider, Mohammad R., author.
- Conference Name:
- 2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2024-08-13 : Novi, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- A sparsely-encoded convolutional autoencoder architecture is proposed in this work for semantic segmentation of unknown terrain. The excellent feature extraction capabilities of the convolutional autoencoder (CAE) is utilized with the computation-efficient Echo State Network (ESN) for faster and efficient encoding, and semantic segmentation of unknown images. The proposed scheme manifests two CAEs trained with image and label data, and an ESN at the latent space of the two CAE to transform the encoded unknown image to semantic segmentation of different regions. The RUGD dataset of off-road images is used for training and validation of the proposed algorithm under variation of hyper-parameters. The proposed algorithm is implemented using Python and PyTorch, and simulation results demonstrate the effectiveness for semantic segmentation
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-01-4077
- Access Restriction:
- Restricted for use by site license
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