## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems
#### 4.3.1 CNN-based Models Used in the Experiment  
ResNet152V2 is a CNN model belonging to the ResNet family, commonly used for image classification tasks in computer vision applications [78; 79]. It is a more advanced and intricate version of the ResNet model with a total of 152 layers and utilizes skip connections (referred to as residual connections) which make it easier for the model to learn from lower layers and improve overall performance. ResNet152V2 employs a bottleneck design, which reduces the number of parameters required in each layer while maintaining the same level of representation power, making it a more efficient model compared to the earlier models in the series. Additionally, ResNet152V2 uses better weight initialization to prevent the vanishing and exploding gradient problems, and normalization approaches, by combining batch normalization and weight normalization, to improve the stability and speed of training [78; 79]. ResNet152V2 also utilizes different training techniques, such as augmentation and stochastic depth to improve the model's generalizability, making it more potent and reliable for image classification tasks compared to other models in the family [78; 79]. Overall, ResNet152V2 performs better than its predecessor and other CNN models in terms of accuracy while requiring fewer parameters. However, it is a very deep and complex model with many layers which demands significant computational resources. These demands can be fulfilled by edge-cloud collaboration as discussed in Section 1. Therefore, for our experiment we have considered ResNet152V2 architecture for feature extraction with a proposed classifier for the classification task, as shown in Figure 9, and parameters of the model are mentioned in Table 4.  
Figure 9: Illustration of ResNet152-based proposed deep learning model for traffic sign recognition in ITS. For a layer '1 \(\times\) 1 conv\(-\)2\(-\)1, 64', 1 \(\times\) 1 is the filter size, 2\(-\)1 is module number, and 64 is the number of filters. The ellipses show that the blocks are repeated. EfficientNet [80] is a family of CNN models that are designed to achieve state-of-the-art accuracy with highly efficient use of computational resources. Since its inception, it has become popular for computer vision applications due to its ability to achieve high accuracy on image classification tasks while using fewer parameters and FLOPs (floating-point operations) than other popular CNN models. EfficientNet utilizes neural architecture search (NAS) to design the baseline model, EfficientNet-B0, which has a better trade-off between parameters and accuracy. The model is then uniformly scaled up in terms of depth, width, and resolution to obtain a family of models, ranging from EfficientNet-B0 (the smallest model) to EfficientNet-B7 (the largest model). Despite using depth-wise convolution to achieve superiority in terms of the number of parameters and FLOPS, EfficientNetV1 had limitations in fully utilizing accelerators, which limited its training and inference speed. EfficientNetV2 [81] mitigates these limitations while ensuring parameter efficiency. It proposes three solutions, first, to adjust the size and regularization progressively during training; second, a non-uniform scaling strategy to add more layers in later stages; and third, the proposed Fused-MBConv in the early stage to improve training speed (introducing a small overhead on parameters and FLOPs). Therefore, in our experiment we have used EfficientNetV2-B0 to achieve state-of-the-art accuracy while ensuring parameter efficiency using the smallest model (B0) in the family and leveraging faster training and inference time (V2). The EfficientNetV2-B0 architecture (adapted from [81; 82]) with our proposed classifier is shown in Figure 10 and the parameters are mentioned in Table 4.  
For this study, we have considered the GTSRB dataset with speed limit signs of seven different classes, 30, 50, 60, 70, 80, 100, and 120. The dataset was divided into 80% for training, 10% for validation, and 10% for testing.