## 5 Conclusions  
This study highlights the potential of multi-backbone (MB) architectures combined with multi-modal data augmentation techniques, like Synchronized Multi-Augmentation (SMA) proposed here, to enhance deep learning model performance and generalization in Edge Intelligence (EI) devices. Using the MobileNetV3 architecture and the CIFAR10 dataset, the effect of various synchronously applied data augmentation techniques on key performance metrics (including validation loss, accuracy, and AUC) was investigated. The experiments reveal that different synchronously applied augmentation techniques impact the validation accuracy and AUC in distinct ways. Spatial augmentation techniques yielded the best results on the original dataset. This trend was consistent across various methods of fusing data from different MobileNetV3 backbones, aiding in feature learning and detection at different locations. \"Move\" transformations show promising results. Thus, other orientation and spatial transformations are a promising direction for future research. Rotation and other spatial transformations should be analyzed separately, as they can be useful for the model in different scenarios and use cases. For the modified dataset, which simulates dark real-world conditions, color data augmentation techniques showed improvements. The \"contrast\" data augmentation technique achieved the best results, likely due to its ability to normalize saturation distribution across images with varying light conditions. Other data augmentation techniques, like \"contrast\" and \"equalize\", create a consistent color distribution, enhancing model performance. By creating additional backbones for synchronously applied data augmentations, the proposed Synchronized Multi-Augmentation (SMA) techniques contribute to improving not only performance but generalization too. By comparing the averaging and dense fusion methods, one can conclude that the averaging fusion method provides more stable results during training, while the dense fusion method (through a fully connected network) demonstrates better metrics with some SMA techniques. Each fusion method affects the results differently depending on the specific augmentation technique used. In practical use for EI devices, MB models can strike a balance between performance gains and computational efficiency. The proposed parallel architecture can be adapted for parallel computing to improve inference times and is easily scalable and modifiable. Utilizing various camera sensors and ISP settings can further enhance model performance. By shifting some data processing responsibilities from the NN to the ISPs and camera sensors, this approach enables the development of smaller models suitable for real-time tasks. Overall, the findings support the feasibility of MB ensembling techniques with SMAs for EI devices. Their improved performance and efficient resource utilization make MB models a promising solution for diverse edge applications. Future research should explore spatial data augmentation techniques and investigate the application of SMAs with different camera sensors and ISP settings. Conceptualization, Y.G. and S.S.; funding acquisition, Y.G. and S.S.; investigation, N.G. and Y.G. ; methodology, N.G. and Y.G. ; project administration, Y.G. and S.S.; resources, S.S.; software, N.G. and Y.G. ; supervision, S.S.; validation, N.G. and Y.G. ; visualization, N.G. and Y.G. ; writing--original draft, N.G. ; writing--review and editing, Y.G. and S.S. All authors have read and agreed to the published version of the manuscript. This research was funded by a U.S. National Science Foundation initiative, by an IMPRESS-U grant as part of exploratory research on new robust machine learning approaches for object detection and classification, and by the NATO Science for Peace and Security Programme under grant id. G6032 as part of the development of Edge Intelligence solutions. Data Availability Statement: The CIFAR-10 dataset used in this work is provided under the MIT License [32].