Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks

We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16, ResNet50) convolutional neural network architectures on the widely a...

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Bibliographic Details
Published inarXiv.org
Main Authors Baumgartl, Hermann, Buettner, Ricardo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.07.2021
Subjects
Artificial neural networks
Computer architecture
Data augmentation
Learning
Lighting
Neural networks
Optimization
Optimization techniques
Recognition
Robotics
Robustness
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Summary:We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16, ResNet50) convolutional neural network architectures on the widely available Event8, Scene15, Stanford40, and MIT67 datasets, we show the generalization ability of our transfer learning strategies. Furthermore, we tested the robustness of our transfer learning strategies under viewpoint and lighting changes using the KTH-Idol2 database. Also, the impact of inference optimization techniques on the general performance and the robustness under different transfer learning strategies is evaluated. Experimental results show that when employing transfer learning, Fine-Tuning in combination with extensive data augmentation improves the general accuracy and robustness in mobile scene recognition. We achieved state-of-the-art results using various baseline convolutional neural networks and showed the robustness against lighting and viewpoint changes in challenging mobile robot place recognition.
ISSN:2331-8422