## 1 Introduction  
Various artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) methods that utilize deep neural networks (DNNs), have been successfully applied across different domains, from the earliest attempts [1, 2, 3, 4, 5, 6] to more recent advances [7, 8, 9]. These methods have typically been investigated on standard datasets such as CIFAR-10, CIFAR-100 [10], and ImageNet [11]. The current practical challenge lies in adapting these approaches for classification tasks in practical applications on Edge Computing (EC) and Edge Intelligence (EI) devices. EC/EI devices generally have limited computational resources, which requires a careful balance between the performance, size,and inference time of the DNNs developed to meet both hardware constraints and user demands in real-time applications. A significant issue is that state-of-the-art (SOTA) AI models are becoming increasingly large, making it impractical to deploy them on EC/EI devices with limited computational capabilities. The research and development of MB DNNs have advanced significantly, offering improved performance and robustness across various applications. The use of multiple backbones in neural networks (NNs) involves integrating several DNN components to enhance the models' capability and efficiency in processing diverse and complex data. This approach has been particularly impactful in fields such as medical image analysis, autonomous driving, hyperspectral image classification, and anomaly detection, as outlined below. In addition, various methods have been developed to optimize Image Signal Processing (ISP) or to utilize different image formats, with the aim of improving the performance of DNNs in image classification tasks. The multi-backbone approach has become a popular topic for solving different tasks. As an example of medical use, [PERSON] et al. (2021) conducted a comparative analysis of MB Mask R-CNN architectures for surgical tool detection [12]. The study proved that MB architectures can effectively address challenges in surgical tool detection, leading to enhanced patient safety and reduced surgical risks. By testing 12 different combinations of CNN backbones and training hyperparameters, the best-performing Mask R-CNN configuration achieved an impressive 87% Average Precision (AP) at an Intersection over Union (IOU) of 0.5. Moreover, a similar approach has been verified in other tasks like autonomous driving and agriculture. [PERSON] et al. (2021) introduced HECNNet, a hybrid ensemble convolutional neural network (CNN) model employing MB feature extractors for soybean disease classification [13]. This approach combines various backbones to enhance feature detection capabilities, resulting in a robust model that efficiently identifies multiple types of image manipulations. On the other hand, [PERSON] et al. (2021) developed MBBNet, an edge IoT computing-based MB network for traffic light detection in autonomous buses [14]. MBBNet integrates three common CNN backbones--normal, residual, and highway (DenseNet) modules. To address the computational load, channel compression is utilized, ensuring high accuracy while maintaining a lightweight model suitable for mobile and embedded hardware. This model achieves high accuracy (>94%) and can process high-resolution images at 14 frames per second on low-power edge devices, which benefits Edge Intelligence solutions. In addition, [PERSON] and [PERSON] (2021) proposed MBMT-Net, a multi-backbone multi-task network architecture for dense prediction tasks [15], which benefits from richer and more diverse features, achieving SOTA performance while maintaining a lower number of parameters compared to traditional multi-task models. MBMT-Net uses multiple pre-trained backbones, whose outputs are concatenated and provided to multi-task heads. The MB approach mitigates the oversaturation problem often encountered in single-shared-backbone models, decreasing the number of parameters and size and enhancing the model's scalability and effectiveness. [PERSON] et al.