## 3 Research Methodology
### Edge Continuity Distortion-Aware Block (ECDAB)  
It is beneficial for classification to extract more complete target information; however, in ERP images, the left and right targets are discontinuous and the standard convolution is the same parameter for global spatial features, which is unfavorable for distorted features. More complete target information can be extracted by feature recombination and segmentation. Group convolution is performed to achieve different convolution kernels between different blocks to alleviate distortion. \begin{table}
\begin{tabular}{c c c} \hline
**Works** & **Year** & **Processing Methods** \\ \hline [PERSON] et al. [37] & 2017 & Transformation of the network obtained in the plane into ERP images \\ [PERSON] et al. [31] & 2018 & Sub-area perspective projection to obtain multiple sub-planes \\ [PERSON] et al. [33] & 2018 & Using the cube mapping method \\ [PERSON] et al. [20] & 2018 & Gnomonic projection to obtain a new convolutional kernel picking position \\ [PERSON] et al. [36] & 2019 & Learn to convert kernel functions for efficient conversion \\ [PERSON] et al. [21] & 2019 & Icosahedral projection, new convolution, and pooling methods \\ [PERSON] et al. [41] & 2021 & Various upscaling factors for different latitudes, automatic selection strategy \\ [PERSON] et al. [40] & 2021 & Attention network that captures global contextual information \\ [PERSON] et al. [24] & 2022 & Transformer feature extractor, sub-area agronomic projection \\ \hline \end{tabular}
\end{table} 
Table 1: A brief summary of studies undertaken in recent years for 360\({}^{\circ}\) omnidirectional images and their approach to dealing with distortion and discontinuity. In Figure 1, it can be seen that the left and right edges of the image are discontinuous. The ERP image disrupts the continuity of the original 360\({}^{\circ}\) omnidirectional images and introduces severe distortion, which will affect the performance of the network. To tackle this situation, we propose ECDAB to process ERP images. Firstly, the continuous edge features of ERP images are extracted through the edge continuity aware block (ECAB). The processed results are then sent to the distortion-aware block (DAB) to alleviate the influence of image distortion. The detailed structure of the ECAB is shown in Figure 3.  
The image in Figure 1 is taken as an example to extract the continuous features of the left and right edges. Firstly, the image is divided into three pieces 1, 2 and 3, then pieces 1 and 3 are flipped horizontally and the flipped results are stitched (enabling the left and right edges of the image to be continuous). This process can be understood as a folding operation on a picture, namely, the left and right sides of the image are folded inwards. In ECAB, the shape of the image obtained after stitching 1 and 3 needs to be the same as 2, which requires that the width of the input image or feature can be divided by four. Then the results after stitching 1 and 3 are stacked with 2 in the channel dimension. The next step is to extract features with continuous information using grouping convolution. Figure 3: Detailed construction of ECAB. Figure 2: Diagram of the overall network structure giving a brief description of the method in this paper, where distortion and discontinuity processing and feature enhancement are performed in the feature-extraction stage. The results of grouping convolution are also segmented and stitched, namely, the features obtained by grouping convolution of the original 1 and 3 are segmented into two pieces 4 and 5. Then they are flipped horizontally--the goal is to ensure that images or features remain in the original order. Then 4, 5 and 6 are stitched together to form features as the input shape. Finally, a convolution operation is performed to mitigate the negative effects of folding. So the left and right edges of features extracted by ECAB contain continuous information.