From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes

Holder, C.J.; Breckon, T.P.; Wei, X.

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.

Citation

Holder, C., Breckon, T., & Wei, X. (2016, December). From On-Road to Off: Transfer Learning within a Deep Convolutional Neural Network for Segmentation and Classification of Off-Road Scenes. Presented at European Conference on Computer Vision Workshops., Amsterdam, The Netherlands

• Presentation Conference Type: Conference Paper (published)
• Conference Name: European Conference on Computer Vision Workshops.
• Acceptance Date: Jul 21, 2016
• Online Publication Date: Sep 18, 2016
• Publication Date: Sep 18, 2016
• Deposit Date: Oct 3, 2016
• Publicly Available Date: Sep 18, 2017
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Pages: 149-162
• Series Title: Lecture notes in computer science
• Series Number: 9913
• Series ISSN: 0302-9743,1611-3349
• Book Title: Computer Vision – ECCV 2016 workshops : Amsterdam, The Netherlands, October 8-10 and 15-16, 2016. Proceedings. Part I.
• ISBN: 9783319466033
• DOI: https://doi.org/10.1007/978-3-319-46604-0_11
• Keywords: automotive vision, off-road semantic understanding, off-road computer vision, off-road scene labelling, terrain segmentation, terrain segments, transfer learning, convolutional neural networks, bag of visual words, deep learning
• Public URL: https://durham-repository.worktribe.com/output/1150276
• Related Public URLs: https://breckon.org/toby/publications/papers/holder16offroad.pdf