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We present four different robust transfer learning and data augmentationstrategies 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 availableEvent8, Scene15, Stanford40, and MIT67 datasets, we show the generalizationability of our transfer learning strategies. Furthermore, we tested therobustness of our transfer learning strategies under viewpoint and lightingchanges using the KTH-Idol2 database. Also, the impact of inferenceoptimization techniques on the general performance and the robustness underdifferent transfer learning strategies is evaluated. Experimental results showthat when employing transfer learning, Fine-Tuning in combination withextensive data augmentation improves the general accuracy and robustness inmobile scene recognition. We achieved state-of-the-art results using variousbaseline convolutional neural networks and showed the robustness againstlighting and viewpoint changes in challenging mobile robot place recognition.

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