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From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.

Holder, C.J. and Breckon, T.P. and Wei, X. (2016) 'From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.', in Computer Vision – ECCV 2016 workshops : Amsterdam, The Netherlands, October 8-10 and 15-16, 2016. Proceedings. Part I. Cham, Switzerland: Springer, pp. 149-162. Lecture notes in computer science. (9913).

Abstract

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the paradigm of transfer learning we analyse the effects on CNN classification, by training and assessing varying levels of prior training on varying sub-sets of our off-road training data. For each of these configurations, we evaluate the network at multiple points during its training cycle, allowing us to analyse in depth exactly how the training process is affected by these variations. Finally, we compare this CNN to a more traditional approach using a feature-driven Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1007/978-3-319-46604-0_11
Publisher statement:The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46604-0_11
Date accepted:21 July 2016
Date deposited:03 October 2016
Date of first online publication:18 September 2016
Date first made open access:18 September 2017

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