## 3 Vehicle Pose Estimation Method
### Mi\(-\)Cnn  
The MI\(-\)CNN network introduced within this paper undertakes a cohesive approach by concurrently executing four critical tasks: frontal vehicle detection, edge point localization, visibility assessment for points, and prediction of yaw angles. The fundamental network structure serves the purpose of extracting hierarchical shared features, a pivotal determinant influencing the model's overall performance. The foundation of the network architecture is established through the utilization of residual convolution modules RI and RC [26]. These modules are fortified with short, quick connections, as visually depicted in Figure 4.  
The initial layer of the network employs 7 \(\times\) 7 \(\times\) 3 \(\times\) 64 convolution kernels with a stride of 2. Following batch regularization and pooling, it generates the initial features. Subsequently, the second layer incorporates one RC model and two RI models, each comprising a convolution kernel count of [64, 64, 256]. This stage yields a feature map denoted as C1. Advancing to the third layer, an additional RI model is introduced, increasing the convolution kernel count to [128, 128, 512]. The resultant output at this stage is marked as C2. The fourth layer incorporates another RI model while doubling the convolution kernel count to [256, 256, 1024]. Given that the feature scale has been reduced to a quarter of its original size, it becomes imperative to append a sufficient number of extraction channels to ensure the preservation of image details at all levels. The resultant shared feature map at the apex of the hierarchy is designated as C3. The ultimate step involves the combination of vehicle feature maps from distinct levels, achieved through the up\(-\)sampling operation. This amalgamation caters to the varied feature\(-\)level requirements inherent in each task. The task of accurately locating points presents challenges when attempting to attain precise positions without initial coordinates. To enhance the network's precision, an iterative prediction strategy is employed. This approach involves refining accuracy through  
Figure 4: The basic network structure based on ResNet. Each frames represents each layer of the network. a sequence of iterative predictions. In the initial prediction of the edge point coordinates {x, y, x, y} on the target vehicle, these coordinates can be rearranged into the format of a 7 \(\times\) 7 \(\times\) 3 feature map. This reorganized representation is then combined with the original 7 \(\times\) 7 \(\times\) 1024 feature map, forming an integrated input for subsequent iterations. This iterative process is iterated K times. With each iteration, the predicted coordinates of the edge points progressively converge towards more accurate values, building upon the improvements achieved in the previous iterations. In the initial iteration, a single convolutional layer and a fully connected layer, both comprising 1024 kernels of dimensions 3 \(\times\) 3 \(\times\) 1024, were employed. The sliding step was set to 1 to facilitate this operation. In the following iterations, the dimensions of the convolution kernels remained consistent at 3 \(\times\) 3, but their count was augmented by 10 to account for the additional 10 point coordinates. The experimental observations revealed that the branch network achieved superior outcomes in terms of both localization speed and accuracy when utilizing K = 3 iterations. The holistic architecture of the convolutional neural network, referred to as MI\(-\)CNN, is visually presented in Figure 5. Commencing with the initial RGB image as input, the foundational feature extraction network orchestrates a reverse progression. This entails in- fusing high\(-\)level features back into the shallow map through up\(-\)sampling. Subsequently, this process culminates in the creation of the shared feature maps, spanning three distinct stages {C\({}_{1}\), C\({}_{2}\), C\({}_{3}\)}.