## 3 Multi-View Modeling  
### Establishment of a Multi-View Model  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn. To improve the small object detection performance of a regression-based deep learning model, a region segmentation operation is applied prior to object detection. Each region of the image can be treated as a separate view and magnified to produce more responses on the feature map of the model, in a manner similar to the imaging magnification process observed in the human eye when approaching an object. The details are shown in Figure 1.  
In Figure 1, the initial imaging process in the human eye is shown by the solid lines, and \(A^{\prime}\) is the real image of object \(A\) after imaging. Suppose that the focus of the eye remains unchanged while object \(A\) moves a certain distance toward the eye; then, the second imaging process is as shown by the dotted lines. In these two processes, the scale of object \(A\) does not change, but the image \(A^{\prime}\) appears larger after the second imaging process. More visual nerve cells are triggered to generate a visual response, and the human eye can perceive more information about the object. If the perceived information is insufficient, the human eye will barely be able to recognize the object or may not even see it at all. Similarly, in object detection, objects are recognized and located based on the information contained in \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Convolution Layer**} & \multicolumn{3}{c}{**Feature Layer**} \\ \hline \multirow{2}{*}{**Layer**} & **Convolution** & \multirow{2}{*}{**Output Scale**} & \multirow{2}{*}{**Default Boxes Ratio**} & \multirow{2}{*}{**Mapping Region Scale**} \\  & **Receptive Field** & & & \\ \hline Conv4\_3 & 92 \(\times\) 92 & 38 \(\times\) 38 & 0.1 & 30 \(\times\) 30 \\ Conv7 & 260 \(\times\) 260 & 19 \(\times\) 19 & 0.2 & 60 \(\times\) 60 \\ Conv8\_2 & 292 \(\times\) 292 & 10 \(\times\) 10 & 0.38 & 114 \(\times\) 114 \\ Conv9\_2 & 356 \(\times\) 356 & 5 \(\times\) 5 & 0.56 & 168 \(\times\) 168 \\ Conv10\_2 & 485 \(\times\) 485 & 3 \(\times\) 3 & 0.74 & 222 \(\times\) 222 \\ Conv11\_2 & 612 \(\times\) 612 & 1 \(\times\) 1 & 0.92 & 276 \(\times\) 276 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Convolution receptive fields and mapping regions of. the default bounding boxes in Single Shot Multibox Detector (SSD)_300 \(\times\) 300. Figure 1: Imaging principle of the human eye. the feature map of the detection model. Therefore, increasing the information captured in the feature map is an effective means of improving object detection performance. To achieve this purpose, a region magnification operation is applied to magnify the RoIs (regions of interest) of the input image. The input image is divided into four equal regions: upper left, upper right, bottom left and bottom right. In additional, because more salient information is usually contained in the central area, a center region is added along with the above four regions. The center region has the same center point as the input image and 1/2 its scale in both the horizontal and vertical directions. Finally, the five regions are interpolated to generate five images of the same size as the original image. Then, when the images are input into the deep learning model, more response will be obtained in the same level feature map. The details of magnification operation are shown in Figure 2.  
The module that performs the operation described above is called the multi-view module. During the detection process, a set of detection results will be acquired for each region after a single detection; thus, five sets of object detection results will be generated. Because the original image is cut into different regions, a cutting phenomenon will be observed in the detection results. Therefore, a merging module is needed at the end of the process.