Enhancing Deep Learning Sustainability by Synchronized Multi Augmentation with Rotations and Multi-Backbone Architectures

Deep learning applications for Edge Intelligence (EI) face challenges in achieving high model performance while maintaining computational efficiency, particularly under varying image orientations and perspectives. This study investigates the synergy of multi-backbone (MB) configurations and Synchronized Multi Augmentation (SMA) to address these challenges by leveraging diverse input representations and spatial transformations. SMA employs synchronously augmented input data across MBs during training, thereby improving feature extraction across diverse representations. The outputs provided by these MBs are merged through different fusion strategies: Averaging Fusion with aggregation of predictions and Dense Fusion with integration of features via a fully connected neural network. It aims to increase model accuracy on previously unseen input data and to reduce computational requirements by minimizing neural network size, particularly advantageous for EI systems characterized by the limited computing resources. This study employed MBs with the MobileNetV3 architecture and the CIFAR-10 dataset to investigate the impact of SMA techniques and different fusion strategies on model robustness and performance. SMA techniques were applied to simulate diverse image orientations, and MB architectures were tested with Averaging and Dense fusion strategies to assess their ability to learn diverse feature representations and improve robustness. The experiments revealed that models augmented with SMA outperformed the baseline MobileNetV3 on modified datasets, achieving higher robustness to orientation variations. Models with Averaging fusion exhibited the most stable performance across datasets, while Dense fusion achieved the highest metrics under specific conditions. Results indicate that SMAs incorporating image transformation adjustments, such as rotation, significantly enhance generalization across varying orientation conditions. This approach enables the production of more stable results using the same pretrained weights in real-world applications by configuring Image Signal Processing (ISP) to effectively use SMA. The findings encourage further exploration of SMA techniques in conjunction with diverse camera sensor configurations and ISP settings to optimize real-world deployments.