## 1 Introduction
(2023) proved that the intermediate representation of visually pleasing images is sub-optimal for downstream computer vision (CV) tasks compared to the RAW image representation [23]. ISP operations, optimized to produce visually pleasing images, were proposed to be optimized toward the end task by learning the parameters of the operations jointly during training. It was found that the RAW detector performs better in low-light conditions compared to the RGB detector. In addition, [PERSON] et al. (2023) demonstrated that detection performance on RAW sensor data is significantly superior to that on standard dynamic range (SDR) data in various situations. They achieved object detection on RAW sensor data, which naturally preserves HDR information from image sensors without extra equipment costs [24]. The authors introduced the ROD dataset for DNN-based object detection algorithms applied to HDR data. The ROD dataset contains numerous annotated instances of day and night driving scenes in a 24-bit dynamic range. Thus, a possible method is to transform RGB images in some way to enhance the model's ability to extract information from the data. [PERSON] et al. (2023) proposed inverting the ISP pipeline, which can convert RGB images from any dataset into their raw counterparts, enabling model training on raw images [25]. Typically, CV models are trained on visually pleasing RGB images, yielding SOTA performance in a wide range of complex vision tasks, such as object detection. The authors proved that such an approach can significantly improve results by transforming the dataset. For ISP-less CV systems, training on these raw images resulted in a 7.1% increase in test accuracy on the Visual Wake Words (VWWs) dataset compared to training with traditional ISP-processed RGB datasets. The development and application of MB DNNs have shown significant advancements across various domains. These architectures enhance feature extraction capabilities, improve performance, and provide robustness while managing computational and resource constraints. The integration of multiple backbones allows for a more diverse and enriched representation of data, leading to SOTA results in numerous applications. In recent papers, ISP and image formats are tuned to preserve as much information as possible and improve the results of CV tasks. The datasets are modified to extract more features and information from the dataset. At the same time, current Edge Intelligence (EI) hardware (Edge Computing hardware with AI abilities), such as microcontrollers, single-board computers, and other IoT devices, typically operates under significant constraints [26; 27]:  
* Limited processing power compared to centralized servers or high-end GPUs;
* Memory availability constraints (RAM and storage capacity) on edge devices;
* Power consumption should be organized efficiently to conserve battery life, especially in mobile or remote applications;
* Real-time processing, because many EI applications require real-time or near-real-time processing, making low latency crucial. Understanding the feasibility of using relatively small DL models for EI devices with multi-modal sensors in real-time applications is crucial, especially given the performance decay observed as DL model sizes decrease in various applications [28; 29; 30]. Various methods of ensembling existing architectures and various augmentation methods are widely accepted practices across various domains to enhance the performance of models, as described above. This research provides experiments and a comparative analysis of different model configurations based on the \"multi-backbone (MB) ensembling\" technique recently proposed by the authors of [31]. The primary objective of this study is to investigate the new Synchronized Multi-Augmentation (SMA) technique in combination with MB ensembling. SMA aims to use different types of simultaneously augmented and transformed input data for training on different backbones (see details in Section 2) to improve model performance.